
# Importing required libraries:

import numpy as np
import pandas as pd

import seaborn as sns
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
import matplotlib.pyplot as plt
from colorama import Style, Fore
import plotly.express as px
import plotly.io as pio

from sklearn.impute import SimpleImputer
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler

from sklearn.model_selection import train_test_split
import warnings


pip install missingno

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Note: you may need to restart the kernel to use updated packages.


!pip install --upgrade threadpoolctl
!pip install --upgrade scikit-learn imbalanced-learn

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import missingno as msno
import json


# setting no limit on the number of columns & rows displayed

pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

#ignoring unnecessary warnings
warnings.filterwarnings("ignore")


#importing all the datsets and assigning respective dataframes

train_df = pd.read_csv('train.csv')
test_df = pd.read_csv('test.csv')
greeks_df = pd.read_csv('greeks.csv')


#checking shape of each dataset

print('Shape of train dataset:', train_df.shape)
print('Shape of test dataset:', test_df.shape)
print('Shape of greeks dataset:', greeks_df.shape)

Shape of train dataset: (617, 58)
Shape of test dataset: (5, 57)
Shape of greeks dataset: (617, 6)


# Examining the greeks dataset

greeks_df.head()


# Statistical summary of string (object) data in train dataset  

greeks_df.describe(include='object').T


# Checking datatypes & null values: greeks dataset

greeks_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 617 entries, 0 to 616
Data columns (total 6 columns):
 #   Column   Non-Null Count  Dtype 
---  ------   --------------  ----- 
 0   Id       617 non-null    object
 1   Alpha    617 non-null    object
 2   Beta     617 non-null    object
 3   Gamma    617 non-null    object
 4   Delta    617 non-null    object
 5   Epsilon  617 non-null    object
dtypes: object(6)
memory usage: 29.0+ KB


# Replacing 'unknowns' as null values in greeks dataset

greeks_df = greeks_df.replace("Unknown", pd.NA)


greeks_df.head(8)


# Calculate missing values for each column (greeks dataset)

print(greeks_df.isnull().sum())

Id           0
Alpha        0
Beta         0
Gamma        0
Delta        0
Epsilon    144
dtype: int64


# Import datatime module to convert the date into number of days

from datetime import datetime


# Converting epsilon column to datatime format

# The errors='coerce' argument is used to handle any values that cannot be converted to datetime format.
# When encountering such values, the 'coerce' option replaces them with *NaT (Not a Time) values*,
# effectively representing missing or invalid dates.

greeks_df['Epsilon'] = pd.to_datetime(greeks_df['Epsilon'], errors='coerce')


greeks_df.head(8)


# Filling the NaT values with current date

greeks_df['Epsilon'].fillna(pd.to_datetime('today'), inplace=True)


greeks_df.head(8)


# New column specifying number of days w.r.t most recent date

greeks_df['Days_Epsilon'] = (pd.to_datetime('today') - greeks_df['Epsilon']).dt.days


greeks_df.head(8)


# Replacing the '0' values in Days_Epsilon' column with median value.

median_days = greeks_df.loc[greeks_df["Days_Epsilon"] != 0, "Days_Epsilon"].median()

greeks_df.loc[greeks_df["Days_Epsilon"] == 0, "Days_Epsilon"] = median_days


greeks_df.head(8)


# Greeks dataset with selected columns (dropping epsilon column)

greeks_df = greeks_df[['Id', 'Alpha', 'Beta', 'Gamma', 'Delta', 'Days_Epsilon']]
greeks_df.head(8)


# Checking missing values for each column (greeks dataset)

print(greeks_df.isnull().sum())

Id              0
Alpha           0
Beta            0
Gamma           0
Delta           0
Days_Epsilon    0
dtype: int64


# Check for duplicate rows in the DataFrame (greeks dataset)
duplicates_g = greeks_df.duplicated()

# Count the number of duplicate rows
num_duplicates_g = duplicates_g.sum()

# Print the duplicate rows and the count
print("Duplicate Rows:")
print(f"Number of Duplicate Rows: {num_duplicates_g}")

Duplicate Rows:
Number of Duplicate Rows: 0


# Statistical summary of numerical data in train dataset

greeks_df.describe()


# One-hot encoding for the categorical coulmns for the Greeks dataset 
# (One-hot encoding because it has nominal variables without an inherent order)

one_hot_encoded_greeks_df = pd.get_dummies(greeks_df, columns=['Alpha', 'Beta', 'Gamma', 'Delta'])
one_hot_encoded_greeks_df.head(8)


# Now greeks dataset has all the numerical columns except 'id'

greeks_df = one_hot_encoded_greeks_df
greeks_df.head(8)


# Examining the train dataset

train_df.head()


# Statistical summary of numerical data in train dataset

train_df.describe().T


# Statistical summary of string (object) data in train dataset

train_df.describe(include='object').T


train_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 617 entries, 0 to 616
Data columns (total 58 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Id      617 non-null    object 
 1   AB      617 non-null    float64
 2   AF      617 non-null    float64
 3   AH      617 non-null    float64
 4   AM      617 non-null    float64
 5   AR      617 non-null    float64
 6   AX      617 non-null    float64
 7   AY      617 non-null    float64
 8   AZ      617 non-null    float64
 9   BC      617 non-null    float64
 10  BD      617 non-null    float64
 11  BN      617 non-null    float64
 12  BP      617 non-null    float64
 13  BQ      557 non-null    float64
 14  BR      617 non-null    float64
 15  BZ      617 non-null    float64
 16  CB      615 non-null    float64
 17  CC      614 non-null    float64
 18  CD      617 non-null    float64
 19  CF      617 non-null    float64
 20  CH      617 non-null    float64
 21  CL      617 non-null    float64
 22  CR      617 non-null    float64
 23  CS      617 non-null    float64
 24  CU      617 non-null    float64
 25  CW      617 non-null    float64
 26  DA      617 non-null    float64
 27  DE      617 non-null    float64
 28  DF      617 non-null    float64
 29  DH      617 non-null    float64
 30  DI      617 non-null    float64
 31  DL      617 non-null    float64
 32  DN      617 non-null    float64
 33  DU      616 non-null    float64
 34  DV      617 non-null    float64
 35  DY      617 non-null    float64
 36  EB      617 non-null    float64
 37  EE      617 non-null    float64
 38  EG      617 non-null    float64
 39  EH      617 non-null    float64
 40  EJ      617 non-null    object 
 41  EL      557 non-null    float64
 42  EP      617 non-null    float64
 43  EU      617 non-null    float64
 44  FC      616 non-null    float64
 45  FD      617 non-null    float64
 46  FE      617 non-null    float64
 47  FI      617 non-null    float64
 48  FL      616 non-null    float64
 49  FR      617 non-null    float64
 50  FS      615 non-null    float64
 51  GB      617 non-null    float64
 52  GE      617 non-null    float64
 53  GF      617 non-null    float64
 54  GH      617 non-null    float64
 55  GI      617 non-null    float64
 56  GL      616 non-null    float64
 57  Class   617 non-null    int64  
dtypes: float64(55), int64(1), object(2)
memory usage: 279.7+ KB


# Bar graph representing the null values:

msno.bar(train_df, figsize = (10, 20), fontsize = 8, sort='ascending', color = "#333366",label_rotation=0)

<Axes: >


# Calculate missing values for each column
missing_values = train_df.isnull().sum()
missing_train = missing_values[missing_values > 0]

# Print columns with missing values
print(missing_train)

BQ    60
CB     2
CC     3
DU     1
EL    60
FC     1
FL     1
FS     2
GL     1
dtype: int64


import pandas as pd
from sklearn.impute import KNNImputer

# Assuming train_df is your DataFrame with missing values

# Separate numerical and categorical columns
numerical_columns = train_df.select_dtypes(include='number').columns
categorical_columns = train_df.select_dtypes(include='object').columns

# Impute missing values in numerical columns using KNN imputer
knn_imputer = KNNImputer(n_neighbors=5)  # You can adjust the value of n_neighbors as per your needs
imputed_numerical_data = knn_imputer.fit_transform(train_df[numerical_columns])
imputed_numerical_df = pd.DataFrame(imputed_numerical_data, columns=numerical_columns)

# Impute missing values in categorical columns with the most frequent value
categorical_imputer = SimpleImputer(strategy='most_frequent')
imputed_categorical_data = categorical_imputer.fit_transform(train_df[categorical_columns])
imputed_categorical_df = pd.DataFrame(imputed_categorical_data, columns=categorical_columns)

# Combine the imputed numerical and categorical DataFrames
imputed_train_df = pd.concat([imputed_numerical_df, imputed_categorical_df], axis=1)


#checking the missing values (deleted later on)
imputed_train_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 617 entries, 0 to 616
Data columns (total 58 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   AB      617 non-null    float64
 1   AF      617 non-null    float64
 2   AH      617 non-null    float64
 3   AM      617 non-null    float64
 4   AR      617 non-null    float64
 5   AX      617 non-null    float64
 6   AY      617 non-null    float64
 7   AZ      617 non-null    float64
 8   BC      617 non-null    float64
 9   BD      617 non-null    float64
 10  BN      617 non-null    float64
 11  BP      617 non-null    float64
 12  BQ      617 non-null    float64
 13  BR      617 non-null    float64
 14  BZ      617 non-null    float64
 15  CB      617 non-null    float64
 16  CC      617 non-null    float64
 17  CD      617 non-null    float64
 18  CF      617 non-null    float64
 19  CH      617 non-null    float64
 20  CL      617 non-null    float64
 21  CR      617 non-null    float64
 22  CS      617 non-null    float64
 23  CU      617 non-null    float64
 24  CW      617 non-null    float64
 25  DA      617 non-null    float64
 26  DE      617 non-null    float64
 27  DF      617 non-null    float64
 28  DH      617 non-null    float64
 29  DI      617 non-null    float64
 30  DL      617 non-null    float64
 31  DN      617 non-null    float64
 32  DU      617 non-null    float64
 33  DV      617 non-null    float64
 34  DY      617 non-null    float64
 35  EB      617 non-null    float64
 36  EE      617 non-null    float64
 37  EG      617 non-null    float64
 38  EH      617 non-null    float64
 39  EL      617 non-null    float64
 40  EP      617 non-null    float64
 41  EU      617 non-null    float64
 42  FC      617 non-null    float64
 43  FD      617 non-null    float64
 44  FE      617 non-null    float64
 45  FI      617 non-null    float64
 46  FL      617 non-null    float64
 47  FR      617 non-null    float64
 48  FS      617 non-null    float64
 49  GB      617 non-null    float64
 50  GE      617 non-null    float64
 51  GF      617 non-null    float64
 52  GH      617 non-null    float64
 53  GI      617 non-null    float64
 54  GL      617 non-null    float64
 55  Class   617 non-null    float64
 56  Id      617 non-null    object 
 57  EJ      617 non-null    object 
dtypes: float64(56), object(2)
memory usage: 279.7+ KB


# Calculate missing values for each column
missing_values = imputed_train_df.isnull().sum()
missing_train = missing_values[missing_values > 0]

# Print columns with missing values
print(missing_train)

Series([], dtype: int64)


# Checking the imputed dataframe of train dataset

imputed_train_df.head()


# Assign the new DataFrame with imputed values to train_df

train_df = imputed_train_df


# Check for duplicate rows in the DataFrame
duplicates_tr = train_df.duplicated()

# Count the number of duplicate rows
num_duplicates = duplicates_tr.sum()

# Print the duplicate rows and the count
print("Duplicate Rows:")
print(f"Number of Duplicate Rows: {num_duplicates}")

Duplicate Rows:
Number of Duplicate Rows: 0


# Assigning columns according to datatypes.

numerical_variables = train_df.select_dtypes(exclude='object').columns
categorical_variables = train_df.select_dtypes(include='object').columns

print ('Numerical columns of train dataset:', numerical_variables)
print ('Categorical columns of train dataset:', categorical_variables)

Numerical columns of train dataset: Index(['AB', 'AF', 'AH', 'AM', 'AR', 'AX', 'AY', 'AZ', 'BC', 'BD ', 'BN', 'BP',
       'BQ', 'BR', 'BZ', 'CB', 'CC', 'CD ', 'CF', 'CH', 'CL', 'CR', 'CS', 'CU',
       'CW ', 'DA', 'DE', 'DF', 'DH', 'DI', 'DL', 'DN', 'DU', 'DV', 'DY', 'EB',
       'EE', 'EG', 'EH', 'EL', 'EP', 'EU', 'FC', 'FD ', 'FE', 'FI', 'FL', 'FR',
       'FS', 'GB', 'GE', 'GF', 'GH', 'GI', 'GL', 'Class'],
      dtype='object')
Categorical columns of train dataset: Index(['Id', 'EJ'], dtype='object')


# Plotting box plots using plotly.express 

def create_boxplot(data,x,y):
    fig = px.box(data, x=x, y=y, color = x, title = f"Box Plots\n{x} vs {y}")
    fig.show()

for feature in numerical_variables:
    create_boxplot(data=train_df,y=feature,x="Class")


# Distribution plot of each feature of train dataset:

# Number of features to plot
num_plots = len(numerical_variables)

# Calculate the number of rows needed based on the number of plots
num_rows = (num_plots + 4) // 3  

# Create a figure and a grid of subplots
fig, ax = plt.subplots(num_rows, 3, figsize=(10, num_rows * 2), dpi=200)

# Flatten the subplot grid into a 1-dimensional array
ax = ax.flatten()

for i, column in enumerate(numerical_variables):
 if i < num_plots:  # Only plot if there are available subplots
    sns.kdeplot(train_df[column], ax=ax[i], color="#333366")
    
    # Set the subplot title
    ax[i].set_title(f'{column} Distribution', size=8)


# Set the main title for the figure    
fig.suptitle('Distribution of Feature\n\n', fontsize=20, fontweight='bold')
plt.tight_layout()


num_plots = len(numerical_variables)  # Number of features to plot
num_rows = (num_plots + 4) // 5  # Calculate the number of rows needed based on the number of plots

# Create a figure and a grid of subplots
fig, ax = plt.subplots(num_rows, 5, figsize=(10, num_rows * 2), dpi=200)
ax = ax.flatten()  # Flatten the subplot grid into a 1-dimensional array

for i, column in enumerate(numerical_variables):
    if i < num_plots:  # Only plot if there are available subplots
        ax[i].hist(train_df[column], color="#333366", bins=20)
        ax[i].set_title(f'{column} Distribution', size=8)  # Set the subplot title

fig.suptitle('Distribution of Features\n\n', fontsize=20, fontweight='bold')  # Set the main title for the figure
plt.tight_layout()  # Adjust the spacing between subplots


train_df.head(8)


# Converting categorical columns in numerical by Label encoding:

from sklearn.preprocessing import LabelEncoder

# Create a LabelEncoder object
label_encoder = LabelEncoder()

# Fit and transform the 'EJ' column in the train dataset
train_df['EJ'] = label_encoder.fit_transform(train_df['EJ'])

# Print the encoded DataFrame
train_df.head()


# Checking the distribution of 'Class' variable(output variable)
class_distribution = train_df['Class'].value_counts()

# Plotting the pie chart
plt.figure(figsize=(8, 6))
plt.pie(class_distribution, labels=class_distribution.index, autopct='%1.1f%%')
plt.title('Distribution of Output Class')
plt.show()


# Dropping the'Id' column

train_df = train_df.drop('Id', axis=1)


train_df.head()


train_df.shape

(617, 57)


# Split the dataframe into features (x) and target variable (y)

x = train_df.drop('Class', axis=1) # features
y = train_df['Class'] # Target variable


# Stratified Sampling: Stratified sampling is used when the dataset is imbalanced or when you want to ensure that each class
# is represented in both the training and test sets


# Splitting the train data into training & validation sets using stratified sampling

x_train, x_valid, y_train, y_valid = train_test_split(x, y, test_size=0.30, stratify=y, random_state=42)


# Print the shapes of the resulting datasets

print("x_train shape:", x_train.shape)
print("x_valid shape:", x_valid.shape)
print("y_train shape:", y_train.shape)
print("y_valid shape:", y_valid.shape)

x_train shape: (431, 56)
x_valid shape: (186, 56)
y_train shape: (431,)
y_valid shape: (186,)


from sklearn.preprocessing import StandardScaler

# Assuming x_train and x_test are the feature sets

# Create the scaler object
scaler = StandardScaler()

# Fit the scaler on the training set
scaler.fit(x_train)

# Transform the training set
x_train_scaled = scaler.transform(x_train)

# Transform the testing set using the same scaler
x_valid_scaled = scaler.transform(x_valid)


# Converting scaled arrays to respective dataframes

x_train_scaled = pd.DataFrame(x_train_scaled, columns=x_train.columns)
x_valid_scaled = pd.DataFrame(x_valid_scaled, columns=x_valid.columns)


x_train_scaled.head()


# Checking the size of splitted data:

print(type(x_train_scaled))
print(x_train_scaled.shape)

print(type(y_train))
print(y_train.shape)

<class 'pandas.core.frame.DataFrame'>
(431, 56)
<class 'pandas.core.series.Series'>
(431,)


x_train_scaled


# importing libraries to balance the target variable by SMOTE:

import os

os.environ['THREADPOOLCTL_USE'] = '0'

# Now run your code with SMOTE
from imblearn.over_sampling import SMOTE

# Create the SMOTE object
smote = SMOTE(random_state=42)

# Apply SMOTE on the scaled training set
x_train_resampled, y_train_resampled = smote.fit_resample(x_train_scaled, y_train)


# Balancing the class of train dataset using oversampling method: SMOTE

from imblearn.over_sampling import SMOTE

# Create the SMOTE object
smote = SMOTE(random_state=42)

# Apply SMOTE on the scaled training set
x_train_resampled, y_train_resampled = smote.fit_resample(x_train_scaled, y_train)


# Checking the resampled input variable of training set

x_train_resampled.head(8)


# Checking the resampled target variable of training set

y_train_resampled = pd.DataFrame({'Class': y_train_resampled})
y_train_resampled.head(8)


# Print the shapes of the resulting datasets

print("x_train shape of over-sampled data:", x_train_resampled.shape)
print("y_train shape of over-sampled data:", y_train_resampled.shape)

x_train shape of over-sampled data: (712, 56)
y_train shape of over-sampled data: (712, 1)


# Renaming the balanced dataframes

x_train_processed = x_train_resampled
x_valid_processed = x_valid_scaled
y_train_processed = y_train_resampled
y_valid_processed = y_valid


# checking the distribution of 'y_train_resampled' variable(output variable)
class_distribution = y_train_processed['Class'].value_counts()

# Plotting the pie chart
plt.figure(figsize=(8, 6))
plt.pie(class_distribution, labels=class_distribution.index, autopct='%1.1f%%')
plt.title('Distribution of Output Class')
plt.show()


# Concatenate x_train_resampled and y_train_resampled into a single DataFrame

concatenate_df = pd.concat([x_train_processed, y_train_processed], axis=1)


# Plotting the correlation between each feature & target variable 'class'

# Calculate correlation with Class
corr_with_class = concatenate_df.corr()['Class'].drop('Class')

plt.figure(figsize=(15, 7))
corr_with_class.plot(kind='bar')
plt.xlabel('Features')
plt.ylabel('Correlation with Class')
plt.title('Correlation of Features with Class')
plt.show()


# Select features with correlation >= 0.1 or <= -0.1
selected_features = corr_with_class[(corr_with_class >= 0.1) | (corr_with_class <= -0.1)]

# Plot the selected features
plt.figure(figsize=(15, 7))
selected_features.plot(kind='bar', color=np.where(selected_features >= 0, 'green', 'red'))
plt.xlabel('Features')
plt.ylabel('Correlation with Class')
plt.title('Features with Correlation >= 0.1 or <= -0.1')
plt.show()


# Printing the selected features

selected_columns = selected_features.index.tolist()
print("Selected Columns:")
print(selected_columns)

Selected Columns:
['AB', 'AF', 'AM', 'AR', 'AX', 'BC', 'BD ', 'BN', 'BP', 'BQ', 'BZ', 'CD ', 'CR', 'CU', 'CW ', 'DA', 'DE', 'DH', 'DI', 'DL', 'DU', 'EB', 'EE', 'EH', 'FD ', 'FE', 'FI', 'FL', 'GB', 'GF', 'GI', 'GL', 'EJ']


len(selected_columns)

33


from sklearn.feature_selection import SelectKBest, f_classif

# Number of top features to select
k = 25

# Create the feature selector
selector = SelectKBest(score_func=f_classif, k=k)

# Perform feature selection
x_train_selected = selector.fit_transform(x_train_processed, y_train_processed)

# Get the indices of the selected features
selected_feature_indices = selector.get_support(indices=True)

# Get the column names of the selected features
selected_feature_names = [x_train_processed.columns[idx] for idx in selected_feature_indices]

# Print the column names of the selected features
print("Selected column names:")
for name in selected_feature_names:
    print(name)

Selected column names:
AB
AF
AM
BC
BN
BQ
CD 
CR
CU
CW 
DA
DE
DH
DI
DL
DU
EB
EE
EH
FD 
FE
FL
GF
GL
EJ


# Convert the selected columns lists to sets

Pearson_corr = set(selected_columns)
Kbest = set(selected_feature_names)

# Find the common elements
common_elements = Pearson_corr.intersection(Kbest)

# Print the common elements
print("Common elements:", common_elements)

Common elements: {'CW ', 'DL', 'FL', 'EE', 'AB', 'DI', 'DA', 'CU', 'EB', 'AF', 'GF', 'AM', 'DU', 'EH', 'DH', 'CR', 'BQ', 'FE', 'BN', 'FD ', 'EJ', 'BC', 'DE', 'GL', 'CD '}


# List of columns which are most related to target variable

common_columns = sorted(list(common_elements))
print("Common columns:", common_elements)

Common columns: {'CW ', 'DL', 'FL', 'EE', 'AB', 'DI', 'DA', 'CU', 'EB', 'AF', 'GF', 'AM', 'DU', 'EH', 'DH', 'CR', 'BQ', 'FE', 'BN', 'FD ', 'EJ', 'BC', 'DE', 'GL', 'CD '}


len(common_columns)

25


if isinstance(x_train_processed, pd.DataFrame):
    print("yes")
else:
    print("no")

yes


# Filter the DataFrame columns
x_train_filtered = x_train_processed[common_columns]

# Print the filtered DataFrame
x_train_filtered.head()


#Checking Shape of training & vValidation sets:

print('Shape of training set with all features :', x_train_processed.shape)
print('Shape of training set with best features :', x_train_filtered.shape)
print('Shape of training set (target):', y_train_processed.shape)
print('Shape of validation set:', x_valid_processed.shape)
print('Shape of validation set (target) :', y_valid_processed.shape)

Shape of training set with all features : (712, 56)
Shape of training set with best features : (712, 25)
Shape of training set (target): (712, 1)
Shape of validation set: (186, 56)
Shape of validation set (target) : (186,)


# importing evaluating parameters from sklearn library.

from sklearn.metrics import precision_score, recall_score, accuracy_score, f1_score, classification_report


# importing required libraries for SVM:

from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV, KFold


# Hyperparameter Tuning using GridSearchCV with k-fold Cross-Validation:

# Create the SVM model
model_SVM = SVC()

# Define the hyperparameter grid
param_grid_SVM = {
    'C': [0.1, 1, 10],                                  # Regularization parameter
    'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],     # Kernel type (linear or radial basis function)
    'gamma': [0.001, 0.01, 0.1],                        # Kernel coefficient for 'rbf'
    'degree': [2, 3, 4],
    'coef0': [0.0, 0.5],
}

# Create k-fold cross-validation object (here, k=5)
kfold = KFold(n_splits=5, shuffle=True, random_state=42)

# Create the GridSearchCV object with k-fold cross-validation
grid_search_SVM = GridSearchCV(model_SVM, param_grid_SVM, cv=kfold)


# Perform grid search with k-fold cross-validation to find the best hyperparameters

grid_search_SVM.fit(x_train_filtered, y_train_processed)

GridSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),
             estimator=SVC(),
             param_grid={'C': [0.1, 1, 10], 'coef0': [0.0, 0.5],
                         'degree': [2, 3, 4], 'gamma': [0.001, 0.01, 0.1],
                         'kernel': ['linear', 'poly', 'rbf', 'sigmoid']})

GridSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),
             estimator=SVC(),
             param_grid={'C': [0.1, 1, 10], 'coef0': [0.0, 0.5],
                         'degree': [2, 3, 4], 'gamma': [0.001, 0.01, 0.1],
                         'kernel': ['linear', 'poly', 'rbf', 'sigmoid']})

SVC()

SVC()


# Retrieve the best model with optimized hyperparameters

best_model_SVM = grid_search_SVM.best_estimator_
best_params_SVM = grid_search_SVM.best_params_
print(best_params_SVM)

{'C': 10, 'coef0': 0.0, 'degree': 2, 'gamma': 0.1, 'kernel': 'rbf'}


# Train the best model on the full training set
best_model_SVM.fit(x_train_filtered, y_train_processed)

SVC(C=10, degree=2, gamma=0.1)

SVC(C=10, degree=2, gamma=0.1)


# Assuming x_train_filtered.columns and x_valid_processed.columns are not in the same order
x_valid_processed = x_valid_processed.reindex(columns=x_train_filtered.columns)


# Checking the order of columns of training & validation:

print("Columns in x_train_filtered:", x_train_filtered.columns)
print("Columns in x_valid_processed:", x_valid_processed.columns)

Columns in x_train_filtered: Index(['AB', 'AF', 'AM', 'BC', 'BN', 'BQ', 'CD ', 'CR', 'CU', 'CW ', 'DA',
       'DE', 'DH', 'DI', 'DL', 'DU', 'EB', 'EE', 'EH', 'EJ', 'FD ', 'FE', 'FL',
       'GF', 'GL'],
      dtype='object')
Columns in x_valid_processed: Index(['AB', 'AF', 'AM', 'BC', 'BN', 'BQ', 'CD ', 'CR', 'CU', 'CW ', 'DA',
       'DE', 'DH', 'DI', 'DL', 'DU', 'EB', 'EE', 'EH', 'EJ', 'FD ', 'FE', 'FL',
       'GF', 'GL'],
      dtype='object')


# Make predictions on the test set
y_pred_SVM = best_model_SVM.predict(x_valid_processed)


# Evaluate the model using accuracy and classification report
accuracy_SVM = accuracy_score(y_valid_processed, y_pred_SVM)
accuracy_percentage_SVM = accuracy_SVM * 100

# Print the accuracy
print("Accuracy of SVM Model: {:.2f}%".format(accuracy_percentage_SVM))

# Convert the classification report string to a DataFrame
classification_report_SVM = classification_report(y_valid_processed, y_pred_SVM, output_dict=True)
classification_report_SVM_df = pd.DataFrame(classification_report_SVM).transpose()

# Convert classification report values to percentages
classification_report_SVM_df.iloc[:, :-1] *= 100

# Print the classification report DataFrame
print("Support Vector Machine Classification Report:")
print(classification_report_SVM_df)

Accuracy of SVM Model: 86.56%
Support Vector Machine Classification Report:
              precision     recall   f1-score     support
0.0           90.000000  94.117647  92.012780  153.000000
1.0           65.384615  51.515152  57.627119   33.000000
accuracy      86.559140  86.559140  86.559140    0.865591
macro avg     77.692308  72.816399  74.819949  186.000000
weighted avg  85.632754  86.559140  85.912098  186.000000


# Importing required libraries for DT:

from sklearn.tree import DecisionTreeClassifier


# Create the Decision Tree classifier
model_DT = DecisionTreeClassifier(random_state=42)

# Define the hyperparameter grid
param_grid_DT = {
    'criterion': ['gini', 'entropy'],
    'max_depth': [None, 10, 20, 30],
    'min_samples_split': [2, 5, 10],
    'min_samples_leaf': [1, 2, 4],
}

# Create k-fold cross-validation object (here, k=5)
kfold = KFold(n_splits=5, shuffle=True, random_state=42)

# Create the GridSearchCV object with k-fold cross-validation
grid_search_DT = GridSearchCV(model_DT, param_grid_DT, cv=kfold)


# Perform grid search with k-fold cross-validation to find the best hyperparameters of DT Model

grid_search_DT.fit(x_train_filtered, y_train_processed)

GridSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),
             estimator=DecisionTreeClassifier(random_state=42),
             param_grid={'criterion': ['gini', 'entropy'],
                         'max_depth': [None, 10, 20, 30],
                         'min_samples_leaf': [1, 2, 4],
                         'min_samples_split': [2, 5, 10]})

GridSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),
             estimator=DecisionTreeClassifier(random_state=42),
             param_grid={'criterion': ['gini', 'entropy'],
                         'max_depth': [None, 10, 20, 30],
                         'min_samples_leaf': [1, 2, 4],
                         'min_samples_split': [2, 5, 10]})

DecisionTreeClassifier(random_state=42)

DecisionTreeClassifier(random_state=42)


# Retrieve the best model with optimized hyperparameters: Decision Tree

best_model_DT = grid_search_DT.best_estimator_
best_params_DT = grid_search_DT.best_params_
print(best_params_DT)

{'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 10}


# Train the best model on the full training set
best_model_DT.fit(x_train_filtered, y_train_processed)

# Make predictions on the test set
y_pred_DT = best_model_DT.predict(x_valid_processed)


# Evaluate the model using accuracy and classification report
accuracy_DT = accuracy_score(y_valid_processed, y_pred_DT)
accuracy_percentage_DT = accuracy_DT * 100

# Print the accuracy
print("Accuracy of DT Model: {:.2f}%".format(accuracy_percentage_DT))

# Convert the classification report string to a DataFrame
classification_report_DT = classification_report(y_valid_processed, y_pred_DT, output_dict=True)
classification_report_DT_df = pd.DataFrame(classification_report_DT).transpose()

# Convert classification report values to percentages
classification_report_DT_df.iloc[:, :-1] *= 100

# Print the classification report DataFrame
print("Decision Tree Classification Report:")
print(classification_report_DT_df)

Accuracy of DT Model: 86.02%
Decision Tree Classification Report:
              precision     recall   f1-score     support
0.0           93.793103  88.888889  91.275168  153.000000
1.0           58.536585  72.727273  64.864865   33.000000
accuracy      86.021505  86.021505  86.021505    0.860215
macro avg     76.164844  80.808081  78.070016  186.000000
weighted avg  87.537915  86.021505  86.589469  186.000000


# importing required libraries for RF:

from sklearn.ensemble import RandomForestClassifier


# Create the Random Forest classifier
model_RF = RandomForestClassifier(random_state=42)

# Define the hyperparameter grid
param_grid_RF = {
    'n_estimators': [300], # Number of trees in the forest
    'criterion': ['gini'],
    'max_depth': [30],
    'min_samples_split': [5],
    'min_samples_leaf': [2],
}

# Create k-fold cross-validation object (here, k=5)
kfold = KFold(n_splits=5, shuffle=True, random_state=42)

# Create the GridSearchCV object with k-fold cross-validation
grid_search_RF = GridSearchCV(model_RF, param_grid_RF, cv=kfold)


# Perform grid search with k-fold cross-validation to find the best hyperparameters of the Random Forest Model

grid_search_RF.fit(x_train_filtered, y_train_processed)

GridSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),
             estimator=RandomForestClassifier(random_state=42),
             param_grid={'criterion': ['gini'], 'max_depth': [30],
                         'min_samples_leaf': [2], 'min_samples_split': [5],
                         'n_estimators': [300]})

GridSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),
             estimator=RandomForestClassifier(random_state=42),
             param_grid={'criterion': ['gini'], 'max_depth': [30],
                         'min_samples_leaf': [2], 'min_samples_split': [5],
                         'n_estimators': [300]})

RandomForestClassifier(random_state=42)

RandomForestClassifier(random_state=42)


# Retrieve the best model with optimized hyperparameters: Random Forest

best_model_RF = grid_search_RF.best_estimator_
best_params_RF = grid_search_RF.best_params_
print(best_params_RF)

{'criterion': 'gini', 'max_depth': 30, 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 300}


# Train the best model on the full training set
best_model_RF.fit(x_train_filtered, y_train_processed)

# Make predictions on the test set
y_pred_RF = best_model_RF.predict(x_valid_processed)


# Evaluate the model using accuracy and classification report
accuracy_RF = accuracy_score(y_valid_processed, y_pred_RF)
accuracy_percentage_RF = accuracy_RF * 100

# Print the accuracy
print("Accuracy of Random Forest Model: {:.2f}%".format(accuracy_percentage_RF))

# Convert the classification report string to a DataFrame
classification_report_RF = classification_report(y_valid_processed, y_pred_RF, output_dict=True)
classification_report_RF_df = pd.DataFrame(classification_report_RF).transpose()

# Convert classification report values to percentages
classification_report_RF_df.iloc[:, :-1] *= 100

# Print the classification report DataFrame
print("Random Forest Classification Report:")
print(classification_report_RF_df)

Accuracy of Random Forest Model: 90.32%
Random Forest Classification Report:
              precision     recall   f1-score     support
0.0           95.918367  92.156863  94.000000  153.000000
1.0           69.230769  81.818182  75.000000   33.000000
accuracy      90.322581  90.322581  90.322581    0.903226
macro avg     82.574568  86.987522  84.500000  186.000000
weighted avg  91.183471  90.322581  90.629032  186.000000


# Importing required libraries for logistic regression:

from sklearn.linear_model import LogisticRegression


# Create the Logistic Regression classifier
model_LR = LogisticRegression(random_state=42)

# Define the hyperparameter grid
param_grid_LR = {
    'C': [0.1, 1.0, 10.0],  # Inverse of regularization strength
    'penalty': ['l1', 'l2'], # Regularization type (L1 or L2)
    'solver': ['liblinear', 'saga'] # Solver to use for optimization (depending on the regularization)
}

# Create k-fold cross-validation object (here, k=5)
kfold = KFold(n_splits=5, shuffle=True, random_state=42)

# Create the GridSearchCV object with k-fold cross-validation
grid_search_LR = GridSearchCV(model_LR, param_grid_LR, cv=kfold)


# Perform grid search with k-fold cross-validation to find the best hyperparameters of the Logistic Regression Model

grid_search_LR.fit(x_train_filtered, y_train_processed)

GridSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),
             estimator=LogisticRegression(random_state=42),
             param_grid={'C': [0.1, 1.0, 10.0], 'penalty': ['l1', 'l2'],
                         'solver': ['liblinear', 'saga']})

GridSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),
             estimator=LogisticRegression(random_state=42),
             param_grid={'C': [0.1, 1.0, 10.0], 'penalty': ['l1', 'l2'],
                         'solver': ['liblinear', 'saga']})

LogisticRegression(random_state=42)

LogisticRegression(random_state=42)


# Retrieve the best model with optimized hyperparameters: Logistic Regression

best_model_LR = grid_search_LR.best_estimator_
best_params_LR = grid_search_LR.best_params_
print(best_params_LR)

{'C': 10.0, 'penalty': 'l1', 'solver': 'liblinear'}


# Train the best model on the full training set
best_model_LR.fit(x_train_filtered, y_train_processed)

# Make predictions on the test set
y_pred_LR = best_model_LR.predict(x_valid_processed)


# Evaluate the model using accuracy and classification report
accuracy_LR = accuracy_score(y_valid_processed, y_pred_LR)
accuracy_percentage_LR = accuracy_LR * 100

# Print the accuracy
print("Accuracy of Logistic Regression Model: {:.2f}%".format(accuracy_percentage_LR))

# Convert the classification report string to a DataFrame
classification_report_LR = classification_report(y_valid_processed, y_pred_LR, output_dict=True)
classification_report_LR_df = pd.DataFrame(classification_report_LR).transpose()

# Convert classification report values to percentages
classification_report_LR_df.iloc[:, :-1] *= 100

# Print the classification report DataFrame
print("Logistic Regression Classification Report:")
print(classification_report_LR_df)

Accuracy of Logistic Regression Model: 86.02%
Logistic Regression Classification Report:
              precision     recall   f1-score     support
0.0           95.035461  87.581699  91.156463  153.000000
1.0           57.777778  78.787879  66.666667   33.000000
accuracy      86.021505  86.021505  86.021505    0.860215
macro avg     76.406619  83.184789  78.911565  186.000000
weighted avg  88.425227  86.021505  86.811499  186.000000


# Importing required libraries for KNN:

from sklearn.neighbors import KNeighborsClassifier


# Create the K-Nearest Neighbors classifier
model_KNN = KNeighborsClassifier()

# Define the hyperparameter grid
param_grid_KNN = {
    'n_neighbors': [3, 5, 7],  # Number of neighbors to consider
    'weights': ['uniform', 'distance'],  # Weight function used in prediction
    'p': [1, 2]  # Power parameter for the Minkowski distance
}

# Create k-fold cross-validation object (here, k=5)
kfold = KFold(n_splits=5, shuffle=True, random_state=42)

# Create the GridSearchCV object with k-fold cross-validation
grid_search_KNN = GridSearchCV(model_KNN, param_grid_KNN, cv=kfold)


# Perform grid search with k-fold cross-validation to find the best hyperparameters

grid_search_KNN.fit(x_train_filtered, y_train_processed)

GridSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),
             estimator=KNeighborsClassifier(),
             param_grid={'n_neighbors': [3, 5, 7], 'p': [1, 2],
                         'weights': ['uniform', 'distance']})

GridSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),
             estimator=KNeighborsClassifier(),
             param_grid={'n_neighbors': [3, 5, 7], 'p': [1, 2],
                         'weights': ['uniform', 'distance']})

KNeighborsClassifier()

KNeighborsClassifier()


# Retrieve the best model with optimized hyperparameters: K-Nearest Neighbors

best_model_KNN = grid_search_KNN.best_estimator_
best_params_KNN = grid_search_KNN.best_params_
print(best_params_KNN)

{'n_neighbors': 7, 'p': 1, 'weights': 'distance'}


# Train the best model on the full training set
best_model_KNN.fit(x_train_filtered, y_train_processed)

# Make predictions on the test set
y_pred_KNN = best_model_KNN.predict(x_valid_processed)


# Evaluate the model using accuracy and classification report
accuracy_KNN = accuracy_score(y_valid_processed, y_pred_KNN)
accuracy_percentage_KNN = accuracy_KNN * 100

# Print the accuracy
print("Accuracy of K-Nearest Neighbors Model: {:.2f}%".format(accuracy_percentage_KNN))

# Convert the classification report string to a DataFrame
classification_report_KNN = classification_report(y_valid_processed, y_pred_KNN, output_dict=True)
classification_report_KNN_df = pd.DataFrame(classification_report_KNN).transpose()

# Convert classification report values to percentages
classification_report_KNN_df.iloc[:, :-1] *= 100

# Print the classification report DataFrame
print("K-Nearest Neighbors Classification Report:")
print(classification_report_KNN_df)

Accuracy of K-Nearest Neighbors Model: 84.95%
K-Nearest Neighbors Classification Report:
              precision     recall   f1-score     support
0.0           96.992481  84.313725  90.209790  153.000000
1.0           54.716981  87.878788  67.441860   33.000000
accuracy      84.946237  84.946237  84.946237    0.849462
macro avg     75.854731  86.096257  78.825825  186.000000
weighted avg  89.491989  84.946237  86.170319  186.000000


# Importing required libraries for NB:

from sklearn.naive_bayes import GaussianNB


# Create the Naive Bayes classifier
model_NB = GaussianNB()


# Train the Naive Bayes model on the training set
model_NB.fit(x_train_filtered, y_train_processed)

# Make predictions on the test set
y_pred_NB = model_NB.predict(x_valid_processed)


# Evaluate the model using accuracy and classification report
accuracy_NB = accuracy_score(y_valid_processed, y_pred_NB)
accuracy_percentage_NB = accuracy_NB * 100

# Print the accuracy
print("Accuracy of Naive Bayes Model: {:.2f}%".format(accuracy_percentage_NB))

# Convert the classification report string to a DataFrame
classification_report_NB = classification_report(y_valid_processed, y_pred_NB, output_dict=True)
classification_report_NB_df = pd.DataFrame(classification_report_NB).transpose()

# Convert classification report values to percentages
classification_report_NB_df.iloc[:, :-1] *= 100

# Print the classification report DataFrame
print("Naive Bayes Classification Report:")
print(classification_report_NB_df)

Accuracy of Naive Bayes Model: 87.63%
Naive Bayes Classification Report:
              precision     recall   f1-score     support
0.0           91.139241  94.117647  92.604502  153.000000
1.0           67.857143  57.575758  62.295082   33.000000
accuracy      87.634409  87.634409  87.634409    0.876344
macro avg     79.498192  75.846702  77.449792  186.000000
weighted avg  87.008546  87.634409  87.227024  186.000000


!pip install xgboost

Requirement already satisfied: xgboost in c:\users\apara\anaconda3\lib\site-packages (1.7.6)
Requirement already satisfied: scipy in c:\users\apara\anaconda3\lib\site-packages (from xgboost) (1.7.3)
Requirement already satisfied: numpy in c:\users\apara\anaconda3\lib\site-packages (from xgboost) (1.22.1)


# Importing required libraries for XGBoost:

import xgboost as xgb


# Create the XGBoost classifier

model_XGB = xgb.XGBClassifier(random_state=42)


# Define the hyperparameter grid

param_grid_XGB = {
    'n_estimators': [100, 200, 300],  # Number of boosting rounds
    'learning_rate': [0.1, 0.01, 0.001],  # Step size shrinkage to prevent overfitting
    'max_depth': [3, 5, 7],  # Maximum depth of a tree
}

# Create k-fold cross-validation object (here, k=5)
kfold = KFold(n_splits=5, shuffle=True, random_state=42)

# Create the GridSearchCV object with k-fold cross-validation
grid_search_XGB = GridSearchCV(model_XGB, param_grid_XGB, cv=kfold)


# Perform grid search with k-fold cross-validation to find the best hyperparameters of the XGBoost Model

grid_search_XGB.fit(x_train_filtered, y_train_processed)

GridSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),
             estimator=XGBClassifier(base_score=None, booster=None,
                                     callbacks=None, colsample_bylevel=None,
                                     colsample_bynode=None,
                                     colsample_bytree=None,
                                     early_stopping_rounds=None,
                                     enable_categorical=False, eval_metric=None,
                                     feature_types=None, gamma=None,
                                     gpu_id=None, grow_policy=None,
                                     importance_type=None,
                                     int...
                                     learning_rate=None, max_bin=None,
                                     max_cat_threshold=None,
                                     max_cat_to_onehot=None,
                                     max_delta_step=None, max_depth=None,
                                     max_leaves=None, min_child_weight=None,
                                     missing=nan, monotone_constraints=None,
                                     n_estimators=100, n_jobs=None,
                                     num_parallel_tree=None, predictor=None,
                                     random_state=42, ...),
             param_grid={'learning_rate': [0.1, 0.01, 0.001],
                         'max_depth': [3, 5, 7],
                         'n_estimators': [100, 200, 300]})

GridSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),
             estimator=XGBClassifier(base_score=None, booster=None,
                                     callbacks=None, colsample_bylevel=None,
                                     colsample_bynode=None,
                                     colsample_bytree=None,
                                     early_stopping_rounds=None,
                                     enable_categorical=False, eval_metric=None,
                                     feature_types=None, gamma=None,
                                     gpu_id=None, grow_policy=None,
                                     importance_type=None,
                                     int...
                                     learning_rate=None, max_bin=None,
                                     max_cat_threshold=None,
                                     max_cat_to_onehot=None,
                                     max_delta_step=None, max_depth=None,
                                     max_leaves=None, min_child_weight=None,
                                     missing=nan, monotone_constraints=None,
                                     n_estimators=100, n_jobs=None,
                                     num_parallel_tree=None, predictor=None,
                                     random_state=42, ...),
             param_grid={'learning_rate': [0.1, 0.01, 0.001],
                         'max_depth': [3, 5, 7],
                         'n_estimators': [100, 200, 300]})

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              predictor=None, random_state=42, ...)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              predictor=None, random_state=42, ...)


# Retrieve the best model with optimized hyperparameters: XGBoost

best_model_XGB = grid_search_XGB.best_estimator_
best_params_XGB = grid_search_XGB.best_params_
print(best_params_XGB)

{'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 200}


# Train the best model on the full training set
best_model_XGB.fit(x_train_filtered, y_train_processed)

# Make predictions on the test set
y_pred_XGB = best_model_XGB.predict(x_valid_processed)


# Evaluate the model using accuracy and classification report
accuracy_XGB = accuracy_score(y_valid_processed, y_pred_XGB)
accuracy_percentage_XGB = accuracy_XGB * 100

# Print the accuracy
print("Accuracy of XGBoost Model: {:.2f}%".format(accuracy_percentage_XGB))

# Convert the classification report string to a DataFrame
classification_report_XGB = classification_report(y_valid_processed, y_pred_XGB, output_dict=True)
classification_report_XGB_df = pd.DataFrame(classification_report_XGB).transpose()

# Convert classification report values to percentages
classification_report_XGB_df.iloc[:, :-1] *= 100

# Print the classification report DataFrame
print("XGBoost Classification Report:")
print(classification_report_XGB_df)

Accuracy of XGBoost Model: 90.32%
XGBoost Classification Report:
              precision     recall   f1-score     support
0.0           95.918367  92.156863  94.000000  153.000000
1.0           69.230769  81.818182  75.000000   33.000000
accuracy      90.322581  90.322581  90.322581    0.903226
macro avg     82.574568  86.987522  84.500000  186.000000
weighted avg  91.183471  90.322581  90.629032  186.000000


!pip install catboost

Requirement already satisfied: catboost in c:\users\apara\anaconda3\lib\site-packages (1.2)
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Requirement already satisfied: numpy>=1.16.0 in c:\users\apara\anaconda3\lib\site-packages (from catboost) (1.22.1)
Requirement already satisfied: graphviz in c:\users\apara\anaconda3\lib\site-packages (from catboost) (0.20.1)
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Requirement already satisfied: tenacity>=6.2.0 in c:\users\apara\anaconda3\lib\site-packages (from plotly->catboost) (8.0.1)


# Importing required libraries for Cat Boost:

from catboost import CatBoostClassifier


# Create the CatBoost classifier
model_CatBoost = CatBoostClassifier(random_seed=42, verbose=False)

# Define the hyperparameter grid
param_grid_CatBoost = {
    'iterations': [100, 200, 300],  # Number of boosting rounds
    'learning_rate': [0.1, 0.01, 0.001],  # Step size shrinkage to prevent overfitting
    'depth': [3, 5, 7],  # Maximum depth of a tree
}

# Create k-fold cross-validation object (here, k=5)
kfold = KFold(n_splits=5, shuffle=True, random_state=42)

# Create the GridSearchCV object with k-fold cross-validation
grid_search_CatBoost = GridSearchCV(model_CatBoost, param_grid_CatBoost, cv=kfold)


# Perform grid search with k-fold cross-validation to find the best hyperparameters of the CatBoost Model

grid_search_CatBoost.fit(x_train_filtered, y_train_processed)

GridSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),
             estimator=<catboost.core.CatBoostClassifier object at 0x0000020C0B9D56C0>,
             param_grid={'depth': [3, 5, 7], 'iterations': [100, 200, 300],
                         'learning_rate': [0.1, 0.01, 0.001]})

GridSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),
             estimator=<catboost.core.CatBoostClassifier object at 0x0000020C0B9D56C0>,
             param_grid={'depth': [3, 5, 7], 'iterations': [100, 200, 300],
                         'learning_rate': [0.1, 0.01, 0.001]})

<catboost.core.CatBoostClassifier object at 0x0000020C0B9D56C0>

<catboost.core.CatBoostClassifier object at 0x0000020C0B9D56C0>


# Retrieve the best model with optimized hyperparameters: CatBoost

best_model_CatBoost = grid_search_CatBoost.best_estimator_
best_params_CatBoost = grid_search_CatBoost.best_params_
print(best_params_CatBoost)

{'depth': 7, 'iterations': 200, 'learning_rate': 0.1}


# Train the best model on the full training set
best_model_CatBoost.fit(x_train_filtered, y_train_processed)

# Make predictions on the test set
y_pred_CatBoost = best_model_CatBoost.predict(x_valid_processed)


# Evaluate the model using accuracy and classification report
accuracy_CatBoost = accuracy_score(y_valid_processed, y_pred_CatBoost)
accuracy_percentage_CatBoost = accuracy_CatBoost * 100

# Print the accuracy
print("Accuracy of CatBoost Model: {:.2f}%".format(accuracy_percentage_CatBoost))

# Convert the classification report string to a DataFrame
classification_report_CatBoost = classification_report(y_valid_processed, y_pred_CatBoost, output_dict=True)
classification_report_CatBoost_df = pd.DataFrame(classification_report_CatBoost).transpose()

# Convert classification report values to percentages
classification_report_CatBoost_df.iloc[:, :-1] *= 100

# Print the classification report DataFrame
print("CatBoost Classification Report:")
print(classification_report_CatBoost_df)

Accuracy of CatBoost Model: 88.71%
CatBoost Classification Report:
              precision     recall   f1-score     support
0.0           95.205479  90.849673  92.976589  153.000000
1.0           65.000000  78.787879  71.232877   33.000000
accuracy      88.709677  88.709677  88.709677    0.887097
macro avg     80.102740  84.818776  82.104733  186.000000
weighted avg  89.846443  88.709677  89.118833  186.000000


pip install matplotlib

Requirement already satisfied: matplotlib in c:\users\apara\anaconda3\lib\site-packages (3.7.0)
Requirement already satisfied: contourpy>=1.0.1 in c:\users\apara\anaconda3\lib\site-packages (from matplotlib) (1.0.5)
Requirement already satisfied: numpy>=1.20 in c:\users\apara\anaconda3\lib\site-packages (from matplotlib) (1.22.1)
Requirement already satisfied: fonttools>=4.22.0 in c:\users\apara\anaconda3\lib\site-packages (from matplotlib) (4.25.0)
Requirement already satisfied: pyparsing>=2.3.1 in c:\users\apara\anaconda3\lib\site-packages (from matplotlib) (3.0.9)
Requirement already satisfied: cycler>=0.10 in c:\users\apara\anaconda3\lib\site-packages (from matplotlib) (0.11.0)
Requirement already satisfied: kiwisolver>=1.0.1 in c:\users\apara\anaconda3\lib\site-packages (from matplotlib) (1.4.4)
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Requirement already satisfied: python-dateutil>=2.7 in c:\users\apara\anaconda3\lib\site-packages (from matplotlib) (2.8.2)
Requirement already satisfied: six>=1.5 in c:\users\apara\anaconda3\lib\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)
Note: you may need to restart the kernel to use updated packages.


# List of model names and their corresponding accuracies
model_names = ['Logistic Regression', 'Naive Bayes', 'Decision Tree', 'Random Forest', 'K-Nearest Neighbors', 'XGBoost', 'CatBoost']
accuracies = [accuracy_percentage_LR, accuracy_percentage_NB, accuracy_percentage_DT, accuracy_percentage_RF,accuracy_percentage_KNN, accuracy_percentage_XGB, accuracy_percentage_CatBoost]

# Find the index of the highest accuracy
highest_accuracy_index = np.argmax(accuracies)

# Plotting the accuracy of different models
plt.figure(figsize=(10, 6))
bars = plt.bar(model_names, accuracies, color='MediumBlue')
plt.ylim(0, 100)
plt.xlabel('Models')
plt.ylabel('Accuracy (%)')
plt.title('Model Accuracy Comparison')
plt.xticks(rotation=45, ha='right')

# Displaying the accuracy values above each bar
for i, v in enumerate(accuracies):
    plt.text(i, v + 1, "{:.2f}%".format(v), ha='center', va='bottom', fontsize=9, fontweight='bold')

# Set a different color for the highest accuracy bar
bars[highest_accuracy_index].set_color('Red')

plt.tight_layout()
plt.show()


# Checking the features of test dataset:

test_df.head()


# checking datatypes & null values of test dataset:

test_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 57 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Id      5 non-null      object 
 1   AB      5 non-null      float64
 2   AF      5 non-null      float64
 3   AH      5 non-null      float64
 4   AM      5 non-null      float64
 5   AR      5 non-null      float64
 6   AX      5 non-null      float64
 7   AY      5 non-null      float64
 8   AZ      5 non-null      float64
 9   BC      5 non-null      float64
 10  BD      5 non-null      float64
 11  BN      5 non-null      float64
 12  BP      5 non-null      float64
 13  BQ      5 non-null      float64
 14  BR      5 non-null      float64
 15  BZ      5 non-null      float64
 16  CB      5 non-null      float64
 17  CC      5 non-null      float64
 18  CD      5 non-null      float64
 19  CF      5 non-null      float64
 20  CH      5 non-null      float64
 21  CL      5 non-null      float64
 22  CR      5 non-null      float64
 23  CS      5 non-null      float64
 24  CU      5 non-null      float64
 25  CW      5 non-null      float64
 26  DA      5 non-null      float64
 27  DE      5 non-null      float64
 28  DF      5 non-null      float64
 29  DH      5 non-null      float64
 30  DI      5 non-null      float64
 31  DL      5 non-null      float64
 32  DN      5 non-null      float64
 33  DU      5 non-null      float64
 34  DV      5 non-null      float64
 35  DY      5 non-null      float64
 36  EB      5 non-null      float64
 37  EE      5 non-null      float64
 38  EG      5 non-null      float64
 39  EH      5 non-null      float64
 40  EJ      5 non-null      object 
 41  EL      5 non-null      float64
 42  EP      5 non-null      float64
 43  EU      5 non-null      float64
 44  FC      5 non-null      float64
 45  FD      5 non-null      float64
 46  FE      5 non-null      float64
 47  FI      5 non-null      float64
 48  FL      5 non-null      float64
 49  FR      5 non-null      float64
 50  FS      5 non-null      float64
 51  GB      5 non-null      float64
 52  GE      5 non-null      float64
 53  GF      5 non-null      float64
 54  GH      5 non-null      float64
 55  GI      5 non-null      float64
 56  GL      5 non-null      float64
dtypes: float64(55), object(2)
memory usage: 2.4+ KB


# Dropping the column of id as not required:

test_x = test_df.drop("Id", axis = 1)
test_x.head()


# Fit and transform the 'EJ' column in the train dataset
test_x['EJ'] = label_encoder.fit_transform(test_x['EJ'])

# Print the encoded DataFrame
test_x.head()


# Assuming x_train.columns and test_x.columns are not in the same order

test_x = test_x.reindex(columns=x_train.columns)


# Scale the test_x data using the scaler
test_x_scaled = scaler.transform(test_x)

# Convert the scaled array back to a DataFrame
test_x_scaled_df = pd.DataFrame(test_x_scaled, columns=test_x.columns)

# Print the scaled DataFrame
test_x_scaled_df.head()


# checking test_x_scaled_df dataframe

test_x_scaled_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 56 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   AB      5 non-null      float64
 1   AF      5 non-null      float64
 2   AH      5 non-null      float64
 3   AM      5 non-null      float64
 4   AR      5 non-null      float64
 5   AX      5 non-null      float64
 6   AY      5 non-null      float64
 7   AZ      5 non-null      float64
 8   BC      5 non-null      float64
 9   BD      5 non-null      float64
 10  BN      5 non-null      float64
 11  BP      5 non-null      float64
 12  BQ      5 non-null      float64
 13  BR      5 non-null      float64
 14  BZ      5 non-null      float64
 15  CB      5 non-null      float64
 16  CC      5 non-null      float64
 17  CD      5 non-null      float64
 18  CF      5 non-null      float64
 19  CH      5 non-null      float64
 20  CL      5 non-null      float64
 21  CR      5 non-null      float64
 22  CS      5 non-null      float64
 23  CU      5 non-null      float64
 24  CW      5 non-null      float64
 25  DA      5 non-null      float64
 26  DE      5 non-null      float64
 27  DF      5 non-null      float64
 28  DH      5 non-null      float64
 29  DI      5 non-null      float64
 30  DL      5 non-null      float64
 31  DN      5 non-null      float64
 32  DU      5 non-null      float64
 33  DV      5 non-null      float64
 34  DY      5 non-null      float64
 35  EB      5 non-null      float64
 36  EE      5 non-null      float64
 37  EG      5 non-null      float64
 38  EH      5 non-null      float64
 39  EL      5 non-null      float64
 40  EP      5 non-null      float64
 41  EU      5 non-null      float64
 42  FC      5 non-null      float64
 43  FD      5 non-null      float64
 44  FE      5 non-null      float64
 45  FI      5 non-null      float64
 46  FL      5 non-null      float64
 47  FR      5 non-null      float64
 48  FS      5 non-null      float64
 49  GB      5 non-null      float64
 50  GE      5 non-null      float64
 51  GF      5 non-null      float64
 52  GH      5 non-null      float64
 53  GI      5 non-null      float64
 54  GL      5 non-null      float64
 55  EJ      5 non-null      float64
dtypes: float64(56)
memory usage: 2.3 KB


# Assuming x_train_filtered.columns and test_x_scaled_df.columns are not in the same order

test_x_scaled_df = test_x_scaled_df.reindex(columns=x_train_filtered.columns)


# Predicting the outputs on the features of test_x_scaled_df

test_predictions = best_model_RF.predict_proba(test_x_scaled_df)
test_preds = test_predictions[:, 1]
test_preds

array([0.3309246, 0.3309246, 0.3309246, 0.3309246, 0.3309246])


# Probability of the class to be 0 or 1 for the given test dataset:

sub_df = pd.DataFrame({'Id': test_df['Id'], 'Class_0': 1-test_preds, 'Class_1': test_preds})
sub_df.to_csv('test.csv', index=False)
sub_df.head()

	Id	Alpha	Beta	Gamma	Delta	Epsilon
0	000ff2bfdfe9	B	C	G	D	3/19/2019
1	007255e47698	A	C	M	B	<NA>
2	013f2bd269f5	A	C	M	B	<NA>
3	043ac50845d5	A	C	M	B	<NA>
4	044fb8a146ec	D	B	F	B	3/25/2020
5	04517a3c90bd	A	C	M	B	10/1/2019
6	049232ca8356	A	C	M	B	5/29/2019
7	057287f2da6d	A	C	M	B	4/24/2019

	Id	Alpha	Beta	Gamma	Delta	Epsilon
0	000ff2bfdfe9	B	C	G	D	2019-03-19
1	007255e47698	A	C	M	B	NaT
2	013f2bd269f5	A	C	M	B	NaT
3	043ac50845d5	A	C	M	B	NaT
4	044fb8a146ec	D	B	F	B	2020-03-25
5	04517a3c90bd	A	C	M	B	2019-10-01
6	049232ca8356	A	C	M	B	2019-05-29
7	057287f2da6d	A	C	M	B	2019-04-24

	Id	Alpha	Beta	Gamma	Delta	Epsilon
0	000ff2bfdfe9	B	C	G	D	2019-03-19 00:00:00.000000
1	007255e47698	A	C	M	B	2023-08-04 17:24:18.033964
2	013f2bd269f5	A	C	M	B	2023-08-04 17:24:18.033964
3	043ac50845d5	A	C	M	B	2023-08-04 17:24:18.033964
4	044fb8a146ec	D	B	F	B	2020-03-25 00:00:00.000000
5	04517a3c90bd	A	C	M	B	2019-10-01 00:00:00.000000
6	049232ca8356	A	C	M	B	2019-05-29 00:00:00.000000
7	057287f2da6d	A	C	M	B	2019-04-24 00:00:00.000000

	Id	Alpha	Beta	Gamma	Delta	Epsilon	Days_Epsilon
0	000ff2bfdfe9	B	C	G	D	2019-03-19 00:00:00.000000	1599
1	007255e47698	A	C	M	B	2023-08-04 17:24:18.033964	0
2	013f2bd269f5	A	C	M	B	2023-08-04 17:24:18.033964	0
3	043ac50845d5	A	C	M	B	2023-08-04 17:24:18.033964	0
4	044fb8a146ec	D	B	F	B	2020-03-25 00:00:00.000000	1227
5	04517a3c90bd	A	C	M	B	2019-10-01 00:00:00.000000	1403
6	049232ca8356	A	C	M	B	2019-05-29 00:00:00.000000	1528
7	057287f2da6d	A	C	M	B	2019-04-24 00:00:00.000000	1563

	Id	Alpha	Beta	Gamma	Delta	Epsilon	Days_Epsilon
0	000ff2bfdfe9	B	C	G	D	2019-03-19 00:00:00.000000	1599
1	007255e47698	A	C	M	B	2023-08-04 17:24:18.033964	1467
2	013f2bd269f5	A	C	M	B	2023-08-04 17:24:18.033964	1467
3	043ac50845d5	A	C	M	B	2023-08-04 17:24:18.033964	1467
4	044fb8a146ec	D	B	F	B	2020-03-25 00:00:00.000000	1227
5	04517a3c90bd	A	C	M	B	2019-10-01 00:00:00.000000	1403
6	049232ca8356	A	C	M	B	2019-05-29 00:00:00.000000	1528
7	057287f2da6d	A	C	M	B	2019-04-24 00:00:00.000000	1563

Goal of the Competition¶

Exploratory Data Analysis: Greeks Dataset:¶

Exploratory Data Analysis: Training dataset¶

Visualizing numerical variables¶

Splitting train data into Training and Validation sets¶

Scaling the feature set (x) of training dataset using Standard Scaler¶

Using SMOTE method, oversampling the training set.¶

Feature selection: Training set¶

(A) Pearson correlation matrix¶

(B) K-Best Method¶

Feature Selection: Top 20 in both¶

Building models, Hypertuning & Evaluating¶

(A) Support Vector Machines¶

(B) Decision Tree Model¶

(C) Random Forest Model¶

(D) Logistic Regression Model¶

(E) K-Nearest Neighbors Model (KNN)¶

(F) Naive Bayes Model¶

(G) XGBoost Model¶

(H) Cat Boost Model¶

Comparing all the models on the basis of Accuracy:¶

Prediction on 'Test' Dataset:¶

	Days_Epsilon
count	617.000000
mean	1444.727715
std	327.708609
min	1039.000000
25%	1192.000000
50%	1467.000000
75%	1563.000000
max	4096.000000

	Id	AB	AF	AH	AM	AR	AX	AY	AZ	BC	BD	BN	BP	BQ	BR	BZ	CB	CC	CD	CF	CH	CL	CR	CS	CU	CW	DA	DE	DF	DH	DI	DL	DN	DU	DV	DY	EB	EE	EG	EH	EJ	EL	EP	EU	FC	FD	FE	FI	FL	FR	FS	GB	GE	GF	GH	GI	GL	Class
0	000ff2bfdfe9	0.209377	3109.03329	85.200147	22.394407	8.138688	0.699861	0.025578	9.812214	5.555634	4126.58731	22.5984	175.638726	152.707705	823.928241	257.432377	47.223358	0.563481	23.387600	4.851915	0.023482	1.050225	0.069225	13.784111	1.302012	36.205956	69.08340	295.570575	0.23868	0.284232	89.245560	84.31664	29.657104	5.310690	1.74307	23.187704	7.294176	1.987283	1433.166750	0.949104	B	30.879420	78.526968	3.828384	13.394640	10.265073	9028.291921	3.583450	7.298162	1.73855	0.094822	11.339138	72.611063	2003.810319	22.136229	69.834944	0.120343	1
1	007255e47698	0.145282	978.76416	85.200147	36.968889	8.138688	3.632190	0.025578	13.517790	1.229900	5496.92824	19.4205	155.868030	14.754720	51.216883	257.432377	30.284345	0.484710	50.628208	6.085041	0.031442	1.113875	1.117800	28.310953	1.357182	37.476568	70.79836	178.553100	0.23868	0.363489	110.581815	75.74548	37.532000	0.005518	1.74307	17.222328	4.926396	0.858603	1111.287150	0.003042	A	109.125159	95.415086	52.260480	17.175984	0.296850	6785.003474	10.358927	0.173229	0.49706	0.568932	9.292698	72.611063	27981.562750	29.135430	32.131996	21.978000	0
2	013f2bd269f5	0.470030	2635.10654	85.200147	32.360553	8.138688	6.732840	0.025578	12.824570	1.229900	5135.78024	26.4825	128.988531	219.320160	482.141594	257.432377	32.563713	0.495852	85.955376	5.376488	0.036218	1.050225	0.700350	39.364743	1.009611	21.459644	70.81970	321.426625	0.23868	0.210441	120.056438	65.46984	28.053464	1.289739	1.74307	36.861352	7.813674	8.146651	1494.076488	0.377208	B	109.125159	78.526968	5.390628	224.207424	8.745201	8338.906181	11.626917	7.709560	0.97556	1.198821	37.077772	88.609437	13676.957810	28.022851	35.192676	0.196941	0
3	043ac50845d5	0.252107	3819.65177	120.201618	77.112203	8.138688	3.685344	0.025578	11.053708	1.229900	4169.67738	23.6577	237.282264	11.050410	661.518640	257.432377	15.201914	0.717882	88.159360	2.347652	0.029054	1.400300	0.636075	41.116960	0.722727	21.530392	47.27586	196.607985	0.23868	0.292431	139.824570	71.57120	24.354856	2.655345	1.74307	52.003884	7.386060	3.813326	15691.552180	0.614484	B	31.674357	78.526968	31.323372	59.301984	7.884336	10965.766040	14.852022	6.122162	0.49706	0.284466	18.529584	82.416803	2094.262452	39.948656	90.493248	0.155829	0
4	044fb8a146ec	0.380297	3733.04844	85.200147	14.103738	8.138688	3.942255	0.054810	3.396778	102.151980	5728.73412	24.0108	324.546318	149.717165	6074.859475	257.432377	82.213495	0.536467	72.644264	30.537722	0.025472	1.050225	0.693150	31.724726	0.827550	34.415360	74.06532	200.178160	0.23868	0.207708	97.920120	52.83888	26.019912	1.144902	1.74307	9.064856	7.350720	3.490846	1403.656300	0.164268	B	109.125159	91.994825	51.141336	29.102640	4.274640	16198.049590	13.666727	8.153058	48.50134	0.121914	16.408728	146.109943	8524.370502	45.381316	36.262628	0.096614	1

	count	mean	std	min	25%	50%	75%	max
AB	617.0	0.477149	0.468388	0.081187	0.252107	0.354659	0.559763	6.161666
AF	617.0	3502.013221	2300.322717	192.593280	2197.345480	3120.318960	4361.637390	28688.187660
AH	617.0	118.624513	127.838950	85.200147	85.200147	85.200147	113.739540	1910.123198
AM	617.0	38.968552	69.728226	3.177522	12.270314	20.533110	39.139886	630.518230
AR	617.0	10.128242	10.518877	8.138688	8.138688	8.138688	8.138688	178.943634
AX	617.0	5.545576	2.551696	0.699861	4.128294	5.031912	6.431634	38.270880
AY	617.0	0.060320	0.416817	0.025578	0.025578	0.025578	0.036845	10.315851
AZ	617.0	10.566447	4.350645	3.396778	8.129580	10.461320	12.969516	38.971568
BC	617.0	8.053012	65.166943	1.229900	1.229900	1.229900	5.081244	1463.693448
BD	617.0	5350.388655	3021.326641	1693.624320	4155.702870	4997.960730	6035.885700	53060.599240
BN	617.0	21.419492	3.478278	9.886800	19.420500	21.186000	23.657700	29.307300
BP	617.0	231.322223	183.992505	72.948951	156.847239	193.908816	247.803462	2447.810550
BQ	557.0	98.328737	96.479371	1.331155	27.834425	61.642115	134.009015	344.644105
BR	617.0	1218.133238	7575.293707	51.216883	424.990642	627.417402	975.649259	179250.252900
BZ	617.0	550.632525	2076.371275	257.432377	257.432377	257.432377	257.432377	50092.459300
CB	615.0	77.104151	159.049302	12.499760	23.317567	42.554330	77.310097	2271.436167
CC	614.0	0.688801	0.263994	0.176874	0.563688	0.658715	0.772206	4.103032
CD	617.0	90.251735	51.585130	23.387600	64.724192	79.819104	99.813520	633.534408
CF	617.0	11.241064	13.571133	0.510888	5.066306	9.123000	13.565901	200.967526
CH	617.0	0.030615	0.014808	0.003184	0.023482	0.027860	0.034427	0.224074
CL	617.0	1.403761	1.922210	1.050225	1.050225	1.050225	1.228445	31.688153
CR	617.0	0.742262	0.281195	0.069225	0.589575	0.730800	0.859350	3.039675
CS	617.0	36.917590	17.266347	13.784111	29.782467	34.835130	40.529401	267.942823
CU	617.0	1.383792	0.538717	0.137925	1.070298	1.351665	1.660617	4.951507
CW	617.0	27.165653	14.645993	7.030640	7.030640	36.019104	37.935832	64.521624
DA	617.0	51.128326	21.210888	6.906400	37.942520	49.180940	61.408760	210.330920
DE	617.0	401.901299	317.745623	35.998895	188.815690	307.509595	507.896200	2103.405190
DF	617.0	0.633884	1.912384	0.238680	0.238680	0.238680	0.238680	37.895013
DH	617.0	0.367002	0.112989	0.040995	0.295164	0.358023	0.426348	1.060404
DI	617.0	146.972099	86.084419	60.232470	102.703553	130.050630	165.836955	1049.168078
DL	617.0	94.795377	28.243187	10.345600	78.232240	96.264960	110.640680	326.236200
DN	617.0	26.370568	8.038825	6.339496	20.888264	25.248800	30.544224	62.808096
DU	616.0	1.802900	9.034721	0.005518	0.005518	0.251741	1.058690	161.355315
DV	617.0	1.924830	1.484555	1.743070	1.743070	1.743070	1.743070	25.192930
DY	617.0	26.388989	18.116679	0.804068	14.715792	21.642456	34.058344	152.355164
EB	617.0	9.072700	6.200281	4.926396	5.965392	8.149404	10.503048	94.958580
EE	617.0	3.064778	2.058344	0.286201	1.648679	2.616119	3.910070	18.324926
EG	617.0	1731.248215	1790.227476	185.594100	1111.160625	1493.817413	1905.701475	30243.758780
EH	617.0	0.305107	1.847499	0.003042	0.003042	0.085176	0.237276	42.569748
EL	557.0	69.582596	38.555707	5.394675	30.927468	71.949306	109.125159	109.125159
EP	617.0	105.060712	68.445620	78.526968	78.526968	78.526968	112.766654	1063.594578
EU	617.0	69.117005	390.187057	3.828384	4.324656	22.641144	49.085352	6501.264480
FC	616.0	71.341526	165.551545	7.534128	25.815384	36.394008	56.714448	3030.655824
FD	617.0	6.930086	64.754262	0.296850	0.296850	1.870155	4.880214	1578.654237
FE	617.0	10306.810737	11331.294051	1563.136688	5164.666260	7345.143424	10647.951650	143224.682300
FI	617.0	10.111079	2.934025	3.583450	8.523098	9.945452	11.516657	35.851039
FL	616.0	5.433199	11.496257	0.173229	0.173229	3.028141	6.238814	137.932739
FR	617.0	3.533905	50.181948	0.497060	0.497060	1.131000	1.512060	1244.227020
FS	615.0	0.421501	1.305365	0.067730	0.067730	0.250601	0.535067	31.365763
GB	617.0	20.724856	9.991907	4.102182	14.036718	18.771436	25.608406	135.781294
GE	617.0	131.714987	144.181524	72.611063	72.611063	72.611063	127.591671	1497.351958
GF	617.0	14679.595398	19352.959387	13.038894	2798.992584	7838.273610	19035.709240	143790.071200
GH	617.0	31.489716	9.864239	9.432735	25.034888	30.608946	36.863947	81.210825
GI	617.0	50.584437	36.266251	0.897628	23.011684	41.007968	67.931664	191.194764
GL	616.0	8.530961	10.327010	0.001129	0.124392	0.337827	21.978000	21.978000
Class	617.0	0.175041	0.380310	0.000000	0.000000	0.000000	0.000000	1.000000

	AB	AF	AH	AM	AR	AX	AY	AZ	BC	BD	BN	BP	BQ	BR	BZ	CB	CC	CD	CF	CH	CL	CR	CS	CU	CW	DA	DE	DF	DH	DI	DL	DN	DU	DV	DY	EB	EE	EG	EH	EL	EP	EU	FC	FD	FE	FI	FL	FR	FS	GB	GE	GF	GH	GI	GL	EJ
0	-0.578847	-0.813207	-0.246596	-0.485677	-0.189234	-0.950187	-0.084117	-0.250628	-0.106195	-0.611691	-0.946420	-0.725135	-0.991933	-0.231473	-0.130952	-0.437169	-0.551490	-0.739397	-0.221702	-0.917126	-0.166308	-0.656098	-0.209843	0.185218	-1.327523	-0.165398	-0.254580	-0.166785	0.736573	0.337447	-0.440000	-0.712331	-0.217917	-0.114165	-0.169187	-0.514413	-1.085553	-0.357101	-0.427051	-0.784791	-0.420340	-0.068096	-0.216354	-0.379963	3.917970	-0.002263	-0.480085	-0.065843	-0.116128	-0.501240	-0.254094	0.402761	-0.202064	0.897986	1.301265	-1.334408
1	0.154287	1.373691	-0.246596	-0.197105	-0.203506	-0.215006	-0.084117	-1.158915	-0.106195	-0.264544	0.268795	-0.347287	-1.016493	0.852010	-0.130952	-0.425427	0.131706	-0.641582	-0.404985	0.398658	-0.166308	-0.722531	0.164961	-0.008372	0.748316	0.751784	-0.809338	-0.181093	-1.038729	0.248368	-0.634861	-2.002368	-0.217917	-0.114165	3.881630	-0.369712	0.903865	-0.123023	-0.427051	1.102205	0.009418	-0.281041	0.050020	-0.379963	2.328843	1.330019	-0.480085	-0.052777	-0.173539	0.961042	-0.396663	-0.755450	0.118290	1.294184	1.301265	-1.334408
2	-0.527698	0.103716	-0.246596	-0.426589	-0.203506	0.612901	-0.038771	-1.739376	-0.106195	0.149485	0.876402	-0.211431	1.184260	-0.170499	0.260843	-0.228503	-1.510491	-0.390058	-0.133024	-0.637172	-0.166308	-0.273908	-0.609973	0.443338	-1.327523	0.507370	-0.895136	-0.181093	-0.211876	-0.391073	0.548620	-0.390940	0.011326	-0.114165	-0.060066	0.244356	-0.799612	-0.207139	0.445951	1.102205	0.192743	-0.304459	-0.412353	0.190773	4.684170	-0.672104	-0.006608	-0.045489	-0.054302	-1.012883	0.028176	-0.669144	-1.294994	-0.831437	-0.815500	0.749396
3	-0.433925	0.772965	-0.246596	-0.401710	-0.203506	-1.076029	-0.084117	-1.739376	-0.090624	0.796538	0.775134	0.620209	-0.997453	0.094638	0.034819	-0.218073	-0.037031	-0.960002	-0.677441	-1.253070	-0.166308	-0.856721	1.693993	-1.438786	-0.428232	2.004963	-0.855011	-0.181093	-1.306240	-0.114855	-0.594843	-1.648749	0.220964	-0.114165	-0.194122	0.775130	0.350235	0.185266	0.533880	-1.404094	-0.420340	0.044610	-0.313442	0.448904	2.767541	-0.986670	0.383630	-0.061318	-0.239782	-0.259681	-0.396663	-0.629923	-1.372102	-0.400550	-0.822730	0.749396
4	-0.050308	-0.519493	-0.183946	0.029572	-0.203506	0.616212	-0.084117	-1.046131	-0.106195	-0.942073	-0.440081	-0.635050	-0.750640	0.887792	-0.130952	-0.382460	-0.512117	0.115519	-0.053980	0.174695	-0.166308	0.421128	-1.506004	-0.793486	-1.327523	-1.299858	0.858791	-0.181093	-0.673941	-0.616229	-0.127236	-0.202941	-0.125521	-0.114165	0.563308	-0.698026	0.565198	-0.837864	-0.295159	-0.791123	-0.420340	-0.230367	-0.399638	-0.328584	-0.578600	-1.069938	-0.225156	-0.065843	-0.239782	-0.908594	-0.396663	-0.740521	2.121993	-1.108181	-0.825925	0.749396

	Id	EJ
0	00eed32682bb	A
1	010ebe33f668	A
2	02fa521e1838	A
3	040e15f562a2	A
4	046e85c7cc7f	A

	AB	AF	AH	AM	AR	AX	AY	AZ	BC	BD	BN	BP	BQ	BR	BZ	CB	CC	CD	CF	CH	CL	CR	CS	CU	CW	DA	DE	DF	DH	DI	DL	DN	DU	DV	DY	EB	EE	EG	EH	EJ	EL	EP	EU	FC	FD	FE	FI	FL	FR	FS	GB	GE	GF	GH	GI	GL
0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	A	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	A	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	A	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	A	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
4	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	A	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

	AB	AF	AH	AM	AR	AX	AY	AZ	BC	BD	BN	BP	BQ	BR	BZ	CB	CC	CD	CF	CH	CL	CR	CS	CU	CW	DA	DE	DF	DH	DI	DL	DN	DU	DV	DY	EB	EE	EG	EH	EJ	EL	EP	EU	FC	FD	FE	FI	FL	FR	FS	GB	GE	GF	GH	GI	GL
0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
4	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

	AB	AF	AH	AM	AR	AX	AY	AZ	BC	BD	BN	BP	BQ	BR	BZ	CB	CC	CD	CF	CH	CL	CR	CS	CU	CW	DA	DE	DF	DH	DI	DL	DN	DU	DV	DY	EB	EE	EG	EH	EL	EP	EU	FC	FD	FE	FI	FL	FR	FS	GB	GE	GF	GH	GI	GL	EJ
0	-0.953938	-1.68874	-0.980614	-0.556082	-1.089497	-2.082763	-0.13559	-2.549916	-0.122038	-1.579278	-6.111085	-1.184475	-1.077923	-0.339968	-0.235943	-0.526381	-2.671424	-1.689007	-0.906916	-2.148923	-0.797822	-2.641951	-2.416079	-2.697119	-1.802673	-2.556235	-1.250301	-0.29423	-3.300414	-1.66411	-3.236556	-3.471453	-0.218693	-1.47925	-1.444573	-1.545561	-1.533729	-0.937579	-0.433332	-1.839692	-1.724393	-0.331081	-0.568678	-0.410914	-0.862248	-3.40328	-0.496826	-0.074137	-0.283943	-2.020124	-0.920747	-0.781901	-3.150813	-1.402969	-0.832533	-1.334408
1	-0.953938	-1.68874	-0.980614	-0.556082	-1.089497	-2.082763	-0.13559	-2.549916	-0.122038	-1.579278	-6.111085	-1.184475	-1.077923	-0.339968	-0.235943	-0.526381	-2.671424	-1.689007	-0.906916	-2.148923	-0.797822	-2.641951	-2.416079	-2.697119	-1.802673	-2.556235	-1.250301	-0.29423	-3.300414	-1.66411	-3.236556	-3.471453	-0.218693	-1.47925	-1.444573	-1.545561	-1.533729	-0.937579	-0.433332	-1.839692	-1.724393	-0.331081	-0.568678	-0.410914	-0.862248	-3.40328	-0.496826	-0.074137	-0.283943	-2.020124	-0.920747	-0.781901	-3.150813	-1.402969	-0.832533	-1.334408
2	-0.953938	-1.68874	-0.980614	-0.556082	-1.089497	-2.082763	-0.13559	-2.549916	-0.122038	-1.579278	-6.111085	-1.184475	-1.077923	-0.339968	-0.235943	-0.526381	-2.671424	-1.689007	-0.906916	-2.148923	-0.797822	-2.641951	-2.416079	-2.697119	-1.802673	-2.556235	-1.250301	-0.29423	-3.300414	-1.66411	-3.236556	-3.471453	-0.218693	-1.47925	-1.444573	-1.545561	-1.533729	-0.937579	-0.433332	-1.839692	-1.724393	-0.331081	-0.568678	-0.410914	-0.862248	-3.40328	-0.496826	-0.074137	-0.283943	-2.020124	-0.920747	-0.781901	-3.150813	-1.402969	-0.832533	-1.334408
3	-0.953938	-1.68874	-0.980614	-0.556082	-1.089497	-2.082763	-0.13559	-2.549916	-0.122038	-1.579278	-6.111085	-1.184475	-1.077923	-0.339968	-0.235943	-0.526381	-2.671424	-1.689007	-0.906916	-2.148923	-0.797822	-2.641951	-2.416079	-2.697119	-1.802673	-2.556235	-1.250301	-0.29423	-3.300414	-1.66411	-3.236556	-3.471453	-0.218693	-1.47925	-1.444573	-1.545561	-1.533729	-0.937579	-0.433332	-1.839692	-1.724393	-0.331081	-0.568678	-0.410914	-0.862248	-3.40328	-0.496826	-0.074137	-0.283943	-2.020124	-0.920747	-0.781901	-3.150813	-1.402969	-0.832533	-1.334408
4	-0.953938	-1.68874	-0.980614	-0.556082	-1.089497	-2.082763	-0.13559	-2.549916	-0.122038	-1.579278	-6.111085	-1.184475	-1.077923	-0.339968	-0.235943	-0.526381	-2.671424	-1.689007	-0.906916	-2.148923	-0.797822	-2.641951	-2.416079	-2.697119	-1.802673	-2.556235	-1.250301	-0.29423	-3.300414	-1.66411	-3.236556	-3.471453	-0.218693	-1.47925	-1.444573	-1.545561	-1.533729	-0.937579	-0.433332	-1.839692	-1.724393	-0.331081	-0.568678	-0.410914	-0.862248	-3.40328	-0.496826	-0.074137	-0.283943	-2.020124	-0.920747	-0.781901	-3.150813	-1.402969	-0.832533	-1.334408

	Id	Class_0	Class_1
0	00eed32682bb	0.669075	0.330925
1	010ebe33f668	0.669075	0.330925
2	02fa521e1838	0.669075	0.330925
3	040e15f562a2	0.669075	0.330925
4	046e85c7cc7f	0.669075	0.330925

	Id	Days_Epsilon	Alpha_A	Alpha_B	Alpha_D	Beta_B	Beta_C	Gamma_F	Gamma_G	Gamma_M	Delta_B	Delta_D
0	000ff2bfdfe9	1599	0	1	0	0	1	0	1	0	0	1
1	007255e47698	1467	1	0	0	0	1	0	0	1	1	0
2	013f2bd269f5	1467	1	0	0	0	1	0	0	1	1	0
3	043ac50845d5	1467	1	0	0	0	1	0	0	1	1	0
4	044fb8a146ec	1227	0	0	1	1	0	1	0	0	1	0
5	04517a3c90bd	1403	1	0	0	0	1	0	0	1	1	0
6	049232ca8356	1528	1	0	0	0	1	0	0	1	1	0
7	057287f2da6d	1563	1	0	0	0	1	0	0	1	1	0

	Id	Days_Epsilon	Alpha_A	Alpha_B	Alpha_D	Beta_B	Beta_C	Gamma_F	Gamma_G	Gamma_M	Delta_B	Delta_D
0	000ff2bfdfe9	1599	0	1	0	0	1	0	1	0	0	1
1	007255e47698	1467	1	0	0	0	1	0	0	1	1	0
2	013f2bd269f5	1467	1	0	0	0	1	0	0	1	1	0
3	043ac50845d5	1467	1	0	0	0	1	0	0	1	1	0
4	044fb8a146ec	1227	0	0	1	1	0	1	0	0	1	0
5	04517a3c90bd	1403	1	0	0	0	1	0	0	1	1	0
6	049232ca8356	1528	1	0	0	0	1	0	0	1	1	0
7	057287f2da6d	1563	1	0	0	0	1	0	0	1	1	0

	AB	AF	AH	AM	AR	AX	AY	AZ	BC	BD	BN	BP	BQ	BR	BZ	CB	CC	CD	CF	CH	CL	CR	CS	CU	CW	DA	DE	DF	DH	DI	DL	DN	DU	DV	DY	EB	EE	EG	EH	EL	EP	EU	FC	FD	FE	FI	FL	FR	FS	GB	GE	GF	GH	GI	GL	EJ
0	-0.578847	-0.813207	-0.246596	-0.485677	-0.189234	-0.950187	-0.084117	-0.250628	-0.106195	-0.611691	-0.946420	-0.725135	-0.991933	-0.231473	-0.130952	-0.437169	-0.551490	-0.739397	-0.221702	-0.917126	-0.166308	-0.656098	-0.209843	0.185218	-1.327523	-0.165398	-0.254580	-0.166785	0.736573	0.337447	-0.440000	-0.712331	-0.217917	-0.114165	-0.169187	-0.514413	-1.085553	-0.357101	-0.427051	-0.784791	-0.420340	-0.068096	-0.216354	-0.379963	3.917970	-0.002263	-0.480085	-0.065843	-0.116128	-0.501240	-0.254094	0.402761	-0.202064	0.897986	1.301265	-1.334408
1	0.154287	1.373691	-0.246596	-0.197105	-0.203506	-0.215006	-0.084117	-1.158915	-0.106195	-0.264544	0.268795	-0.347287	-1.016493	0.852010	-0.130952	-0.425427	0.131706	-0.641582	-0.404985	0.398658	-0.166308	-0.722531	0.164961	-0.008372	0.748316	0.751784	-0.809338	-0.181093	-1.038729	0.248368	-0.634861	-2.002368	-0.217917	-0.114165	3.881630	-0.369712	0.903865	-0.123023	-0.427051	1.102205	0.009418	-0.281041	0.050020	-0.379963	2.328843	1.330019	-0.480085	-0.052777	-0.173539	0.961042	-0.396663	-0.755450	0.118290	1.294184	1.301265	-1.334408
2	-0.527698	0.103716	-0.246596	-0.426589	-0.203506	0.612901	-0.038771	-1.739376	-0.106195	0.149485	0.876402	-0.211431	1.184260	-0.170499	0.260843	-0.228503	-1.510491	-0.390058	-0.133024	-0.637172	-0.166308	-0.273908	-0.609973	0.443338	-1.327523	0.507370	-0.895136	-0.181093	-0.211876	-0.391073	0.548620	-0.390940	0.011326	-0.114165	-0.060066	0.244356	-0.799612	-0.207139	0.445951	1.102205	0.192743	-0.304459	-0.412353	0.190773	4.684170	-0.672104	-0.006608	-0.045489	-0.054302	-1.012883	0.028176	-0.669144	-1.294994	-0.831437	-0.815500	0.749396
3	-0.433925	0.772965	-0.246596	-0.401710	-0.203506	-1.076029	-0.084117	-1.739376	-0.090624	0.796538	0.775134	0.620209	-0.997453	0.094638	0.034819	-0.218073	-0.037031	-0.960002	-0.677441	-1.253070	-0.166308	-0.856721	1.693993	-1.438786	-0.428232	2.004963	-0.855011	-0.181093	-1.306240	-0.114855	-0.594843	-1.648749	0.220964	-0.114165	-0.194122	0.775130	0.350235	0.185266	0.533880	-1.404094	-0.420340	0.044610	-0.313442	0.448904	2.767541	-0.986670	0.383630	-0.061318	-0.239782	-0.259681	-0.396663	-0.629923	-1.372102	-0.400550	-0.822730	0.749396
4	-0.050308	-0.519493	-0.183946	0.029572	-0.203506	0.616212	-0.084117	-1.046131	-0.106195	-0.942073	-0.440081	-0.635050	-0.750640	0.887792	-0.130952	-0.382460	-0.512117	0.115519	-0.053980	0.174695	-0.166308	0.421128	-1.506004	-0.793486	-1.327523	-1.299858	0.858791	-0.181093	-0.673941	-0.616229	-0.127236	-0.202941	-0.125521	-0.114165	0.563308	-0.698026	0.565198	-0.837864	-0.295159	-0.791123	-0.420340	-0.230367	-0.399638	-0.328584	-0.578600	-1.069938	-0.225156	-0.065843	-0.239782	-0.908594	-0.396663	-0.740521	2.121993	-1.108181	-0.825925	0.749396
5	-0.493599	-0.372892	-0.148156	0.086845	-0.203506	1.656063	-0.084117	1.845649	-0.106195	0.752954	-0.136277	0.179512	-0.593107	0.061663	0.260671	-0.260777	0.131682	0.316055	-0.637178	-0.581181	0.105434	-1.425242	0.694937	1.841486	-1.327523	0.821368	0.890185	-0.181093	0.006997	0.614259	-0.503212	-0.259341	-0.217917	-0.114165	-0.810830	-0.030454	-0.300736	0.027032	-0.427051	1.102205	-0.420340	-0.079224	0.597405	-0.379963	-0.260700	-0.248364	-0.480085	-0.057171	-0.217701	1.699537	-0.396663	1.727410	-1.140032	0.197489	1.301265	-1.334408
6	0.103138	0.262438	-0.246596	-0.201165	-0.203506	-0.099099	-0.084117	0.994506	-0.050880	-0.173295	0.775134	-0.261449	0.859735	0.309910	-0.130952	-0.015509	-0.303474	-1.250474	4.781053	-0.133254	-0.166308	0.033644	-0.088515	-0.535366	-1.327523	-0.062283	-0.361939	-0.181093	-0.138918	-0.257560	0.213806	-0.414217	-0.058553	-0.114165	-1.400381	0.081415	-1.026742	-0.041610	0.282656	-0.021718	-0.420340	-0.304459	-0.393724	0.152394	-0.153704	-2.200526	-0.026547	-0.024844	0.069352	-0.134890	-0.396663	-0.306927	1.320363	1.629745	-0.812611	0.749396
7	0.213961	0.080134	0.081135	-0.274809	-0.203506	-0.218318	-0.084117	1.597525	-0.106195	0.534563	0.167527	0.025421	0.224064	0.008408	-0.130952	0.267216	0.327473	-0.066286	-0.065095	-0.385213	-0.017041	0.237179	0.153105	-0.503101	0.720467	0.971292	0.302690	-0.181093	-0.892814	-0.144993	0.080797	-0.690845	0.181171	-0.114165	0.670454	-0.067542	-0.073606	0.399997	0.157044	-1.166922	-0.420340	-0.190574	1.199544	0.229771	-0.530029	-0.551829	0.528207	-0.036521	0.268080	0.367840	-0.233196	-0.529183	0.795876	-0.600082	-0.825954	0.749396
8	-0.510648	-0.843439	-0.246596	-0.396268	-0.203506	-0.860773	-0.070636	-0.158897	-0.106195	-0.651438	-1.655296	-0.427256	-0.726725	0.098616	0.203314	0.916565	-0.634510	-0.192346	-0.854302	-0.189245	-0.166308	-0.134160	-0.409448	-0.395552	1.295435	0.396793	-0.476378	-0.181093	0.031316	-0.376290	0.469086	-0.284407	-0.217917	-0.114165	-0.392367	-0.143540	-0.998351	-0.238733	-0.427051	-0.346208	-0.130500	-0.304459	-0.218621	-0.379963	-0.531535	-0.596238	-0.480085	-0.044898	-0.239782	0.555026	-0.396663	1.821916	0.419274	-0.822834	1.301265	-1.334408
9	-0.519173	-0.461430	-0.246596	-0.289322	-0.203506	-0.751489	0.085010	-0.701764	-0.076410	-0.434568	-1.250224	-0.629755	-1.052292	-0.203587	-0.130952	-0.437169	-0.877615	-0.219996	-0.673612	0.538635	-0.166308	0.961328	0.544140	-0.148187	0.703984	0.141040	-0.973466	-0.181093	-1.014410	-0.019464	-0.226312	-1.094597	-0.217917	-0.114165	1.702909	-0.105845	-0.740801	-0.467864	-0.427051	1.102205	0.977453	-0.244347	-0.232716	-0.379963	0.094599	0.280847	-0.480085	-0.065843	-0.239782	-0.906811	-0.396663	-0.171890	0.418156	-0.689253	1.301265	-1.334408
10	0.682826	0.010062	0.515484	-0.318305	-0.203506	0.215505	-0.084117	0.568935	-0.106195	0.020276	0.876402	-0.018728	0.398212	-0.217860	-0.130952	-0.228110	-0.621657	-0.247200	-0.698560	-0.413208	0.059506	0.916069	-0.196836	-0.352532	-1.327523	0.982049	-0.384091	-0.181093	0.858169	-0.217383	0.585129	-0.215474	-0.208988	-0.114165	0.691686	0.160454	0.222474	-0.428224	0.043993	-1.108934	-0.420340	-0.186841	-0.281309	0.033542	-0.112457	1.972104	-0.359805	-0.065843	-0.239782	0.119149	-0.396663	-0.522176	1.489108	-0.382226	-0.613386	0.749396
11	-0.549010	-0.066405	-0.217147	-0.368171	-0.203506	-0.607434	0.072141	0.024565	-0.019194	-0.146026	0.268795	-0.160619	-0.993257	-0.151082	-0.130952	-0.068085	-0.159779	-0.489211	-0.120056	-0.287229	-0.152912	-0.299581	1.025653	-0.062147	0.628552	-0.591426	0.324727	-0.181093	0.371785	0.286088	0.209260	-0.959864	-0.217917	-0.114165	0.759701	0.068648	-0.783388	-0.406643	-0.427051	0.466913	3.467575	-0.024570	-0.342864	-0.379963	3.105926	-0.196554	-0.480085	-0.039430	0.003109	-0.100572	-0.396663	-0.501335	1.838331	0.023413	1.301265	-1.334408
12	0.572003	-0.033079	-0.246596	0.557994	-0.203506	-0.499806	-0.084117	1.011048	-0.106195	0.507061	-0.440081	-0.192059	-0.775198	-0.052822	-0.130952	-0.281637	0.737280	1.270124	-0.687568	0.426654	-0.108898	0.168893	-0.140086	0.034648	-1.327523	-1.070563	-0.648943	-0.181093	-0.819856	0.705244	0.037630	2.455915	-0.217917	-0.114165	-1.052279	0.790330	1.579172	-0.361040	-0.427051	1.102205	-0.420340	1.685083	-0.236462	-0.379963	-0.345110	-0.742419	-0.480085	-0.050028	-0.239782	-1.553050	0.591823	-0.349253	2.209904	2.546141	1.301265	-1.334408
13	-0.485074	-0.245120	0.457185	-0.034095	-0.203506	0.490370	-0.084117	2.926871	-0.015664	0.195115	-0.541349	-0.072563	-0.730612	-0.159863	-0.130952	8.195646	-0.432636	1.316356	0.002833	0.006723	0.013578	0.686861	0.332794	-1.298971	0.676825	0.249389	-0.196615	-0.181093	-2.011497	-0.986462	-1.521540	-0.687264	-0.217917	-0.114165	-0.347435	-0.197043	-0.152696	0.181600	-0.427051	-0.726004	0.307879	-0.073695	2.383467	-0.379963	0.122313	0.106910	-0.480085	-0.054383	0.091433	-0.621574	-0.396663	1.080484	-0.094038	-1.084961	1.301265	-1.334408
14	-0.459499	-0.574834	-0.246596	-0.348496	-0.203506	0.298296	-0.084117	0.964431	-0.106195	-0.047181	-0.541349	0.127067	-0.837289	-0.151300	-0.130952	-0.435726	0.093481	-0.049414	-0.569743	0.034718	-0.166308	0.089226	-0.555410	-0.072902	0.640763	2.884833	1.134054	-0.181093	-1.136006	-0.225664	-0.755127	-0.527017	-0.217917	-0.114165	-0.727137	-0.560620	-0.428497	0.052463	-0.427051	0.034310	-0.420340	-0.067251	-0.358831	-0.379963	-0.241417	0.399272	-0.480085	-0.055313	-0.239782	0.154804	-0.396663	1.574350	0.654324	-0.337571	1.301265	-1.334408
15	-0.127032	0.797323	-0.246596	-0.082903	-0.203506	0.179078	-0.084117	0.681719	-0.070797	0.261206	0.673866	0.024274	1.644121	-0.203182	-0.130952	-0.010064	-0.303474	0.916176	-0.131789	0.482645	-0.032350	-0.267291	-0.053752	-1.922760	0.641596	2.995702	0.327904	-0.181093	-1.111686	0.520435	-1.647176	0.664545	-0.217917	-0.114165	0.016467	-0.023159	0.064294	0.329671	-0.427051	-0.378591	-0.420340	-0.069575	-0.217192	-0.379963	-0.487441	0.056950	-0.480085	-0.047182	0.219502	1.413410	-0.396663	1.432323	0.759371	1.242430	1.301265	-1.334408
16	-0.340152	0.298213	-0.246596	-0.344004	-0.203506	0.275115	-0.084117	0.666681	-0.106195	0.642492	0.268795	-0.264582	1.118972	-0.219862	-0.130952	0.003580	-0.481856	-0.477616	-0.063119	0.006723	0.028887	-0.027496	0.071490	0.368053	-0.056406	2.046151	-0.626603	-0.181093	1.004084	-0.659455	0.946358	0.535630	-0.104169	-0.114165	-0.645914	-0.683434	-0.440665	-0.009861	-0.031374	1.102205	0.558777	-0.195363	-0.319454	-0.093357	-0.470009	0.465886	-0.097475	-0.065843	-0.116128	-0.309597	-0.396663	-0.602621	0.799229	-0.311239	-0.816894	0.749396
17	-0.144081	1.051117	-0.190398	-0.284484	-0.203506	1.960733	-0.001392	-0.115287	-0.084422	-0.213238	-0.237545	-0.426351	0.029997	-0.154101	-0.130952	-0.400303	0.162275	-0.627683	0.414357	0.482645	-0.166308	-0.920772	-0.054212	-0.331022	0.984436	1.337525	0.109531	0.066310	-0.382110	0.018383	1.045793	-0.287988	-0.077964	-0.114165	-0.020812	0.024873	0.697015	-0.501740	0.094238	0.490752	0.349362	-0.150816	-0.260709	-0.144117	-0.642311	-0.727616	-0.213453	-0.065843	-0.120545	-0.719624	0.833334	-0.541809	1.126660	-0.588508	-0.815829	0.749396
18	-0.621471	-0.376772	-0.246596	-0.455442	-0.203506	-1.821145	-0.084117	-0.602514	0.110900	-0.666144	-1.756564	0.307679	-0.907070	-0.174392	-0.130952	0.135628	-0.497584	-1.250474	-0.865418	-0.217240	-0.166308	0.149307	-0.507753	0.421828	0.729800	-0.155706	-0.582381	-0.181093	0.177231	-0.497731	0.414250	1.493535	-0.217917	-0.114165	-1.400381	-0.698026	-0.633320	-0.299953	-0.427051	-0.435262	-0.420340	-0.304459	-0.395892	-0.379963	-0.175158	-2.200526	-0.480085	-0.055293	-0.230949	-0.909485	-0.396663	0.531907	2.958639	-0.224726	1.301265	-1.334408
19	-0.229329	0.178803	-0.246596	-0.091153	-0.203506	-0.821034	-0.041222	-0.464166	-0.084377	-0.176442	0.673866	-0.645265	0.116922	-0.128732	-0.130952	1.023817	0.000639	0.583040	-0.127837	-0.301227	-0.166308	0.568551	-0.144230	0.389563	-1.327523	1.098150	-0.397242	-0.181093	-0.673941	-0.351511	0.764598	2.191820	-0.019732	-0.114165	-0.343732	-0.698026	-0.884786	-0.287283	0.408268	1.102205	0.035124	-0.304459	-0.267412	0.299101	0.327248	-0.422301	0.065272	-0.052748	-0.111712	-0.571658	-0.396663	-0.297002	-0.630073	-0.608755	-0.813685	0.749396
20	0.111663	0.662252	-0.199177	-0.434191	-0.203506	0.535077	-0.084117	-0.127318	0.069074	0.015557	0.977670	0.762540	2.648425	-0.102684	0.211834	-0.390070	-0.955723	0.690295	-0.165259	1.182530	-0.032350	-0.273378	-0.219628	0.416450	0.293453	-0.873927	-0.608509	-0.181093	-0.163238	-0.079676	0.429176	0.659621	-0.217917	-0.114165	4.246767	0.318531	-1.389745	-0.420465	-0.427051	1.102205	0.559239	0.015998	-0.276529	-0.379963	-0.066699	-0.391770	-0.480085	0.687176	-0.239782	1.408953	-0.396663	-0.733277	1.461170	0.284142	1.301265	-1.334408
21	-0.681144	-0.547013	-0.246596	-0.113008	-0.203506	0.169143	-0.084117	-1.739376	-0.106195	-0.279279	0.167527	0.183748	-0.559871	-0.321829	-0.130952	-0.385478	-0.457346	-0.255822	-0.787732	0.874580	-0.166308	-0.286083	0.011632	-0.599896	1.272691	1.131101	0.508865	-0.181093	-0.844175	-0.986462	-0.692202	-0.738293	-0.217723	-0.114165	-0.389158	-0.598316	-1.097720	-0.250137	-0.282598	-1.144844	0.365909	0.009201	-0.305064	-0.280301	-0.380485	-0.479663	-0.456503	-0.058637	-0.058718	0.620096	-0.396663	-0.559430	-1.007047	-1.067827	-0.140490	0.749396
22	0.120188	-0.939664	-0.246596	-0.104845	-0.203506	-0.542857	0.127904	0.424572	-0.069530	-0.352582	-0.440081	-0.417813	-1.019929	-0.115651	0.309497	-0.022331	0.144712	0.050111	-0.064848	0.622622	-0.020868	-0.336636	0.266260	0.228238	0.894392	0.603896	0.780152	-0.181093	0.614977	0.310465	-0.434846	-0.173398	-0.217917	-0.114165	0.884993	-0.226227	1.240505	-0.570470	-0.427051	1.102205	0.778428	0.039416	1.236161	-0.379963	-0.400215	-0.063325	-0.480085	-0.065843	-0.239782	-0.851546	-0.396663	0.736072	-0.504166	0.085378	1.301265	-1.334408
23	0.120188	0.305847	-0.246596	0.120406	-0.203506	-0.377276	-0.084117	0.346375	-0.106195	0.015730	0.977670	-0.150116	2.074014	-0.102240	-0.130952	0.769692	-0.877615	0.406140	0.388050	-0.217240	-0.166308	-0.204563	-0.050183	-0.449327	0.632935	0.267415	-0.082781	-0.181093	-0.844175	-0.292294	-1.036680	1.900868	-0.007114	-0.114165	-0.833049	0.005417	-0.931429	-0.017827	0.596686	-1.488787	-0.420340	-0.304459	-0.333796	0.370908	0.192873	-0.614742	0.024686	-0.054199	-0.085215	0.276029	-0.396663	0.448426	-0.976875	-0.539796	-0.810841	0.749396
24	-0.058833	0.071624	-0.189723	0.318057	-0.203506	1.162631	-0.084117	-0.394991	-0.106195	0.249973	1.180206	-0.052441	-0.718585	-0.122698	-0.130952	0.121328	-1.216438	-1.250474	1.414760	0.118704	-0.166308	0.067787	-0.297444	0.088423	0.679443	-0.289058	1.335917	-0.181093	-0.163238	-0.571498	0.245304	0.785402	-0.217917	-0.114165	-1.400381	0.035816	-0.661711	-0.210658	-0.427051	1.102205	0.020501	-0.304459	1.058250	-0.379963	-0.494185	-2.200526	-0.480085	-0.049409	-0.058718	-0.850655	-0.396663	1.643507	0.731060	-0.963689	1.301265	-1.334408
25	-0.476549	-1.126763	0.467014	0.428243	-0.190376	0.553291	0.255362	-0.086715	-0.057218	-0.617654	-0.136277	0.006623	2.140964	-0.321829	-0.130952	-0.233095	0.218595	-0.363151	0.389903	-0.161250	0.162845	0.367134	0.059058	1.475817	1.008125	0.736472	0.751203	-0.181093	2.268682	-0.055299	-0.059013	0.727211	-0.217917	-0.114165	-0.400267	-0.302833	-0.793528	-0.099182	-0.427051	1.102205	2.160829	-0.297592	-0.372778	-0.379963	-0.174554	-0.340884	-0.480085	-0.065843	-0.239782	-0.242745	-0.113692	1.209302	-1.173557	0.377928	1.301265	-1.334408
26	-0.433925	0.077585	-0.246596	-0.352384	-0.203506	-0.400457	-0.084117	0.226072	-0.106195	-0.492032	-0.035009	-0.222992	-0.239290	-0.048429	-0.130952	-0.349268	-0.061895	-0.761696	-0.416471	-0.385213	-0.166308	-1.156597	-0.800828	0.561643	-0.348988	-1.166312	-0.035535	-0.181093	-0.357791	0.185550	-0.483955	-0.032845	-0.028466	-0.114165	0.093247	-0.278514	0.208279	15.311691	0.081677	-0.731937	-0.420340	-0.304459	-0.363563	0.142489	-0.207420	-1.064387	0.203223	-0.054461	-0.239782	-0.509262	1.075227	-0.601701	-1.891746	0.922604	-0.820469	0.749396
27	-0.263429	-0.086536	0.552248	-0.273125	-0.203506	-0.569350	0.009025	-1.739376	-0.106195	-0.113400	1.686546	0.435559	-0.789562	-0.120099	-0.130952	-0.158511	0.140156	-0.603675	-0.718074	-0.077263	-0.166308	-0.287141	-0.078155	-0.578386	-1.327523	-0.646763	-0.505435	-0.181093	-0.187557	-0.290345	-0.177060	0.491764	-0.104169	-0.114165	0.432708	0.739259	-0.903037	0.071915	0.295217	-1.431666	0.884020	-0.175924	1.321125	0.358527	-0.240852	0.687933	0.037038	-0.055375	-0.116128	0.831349	-0.396663	-0.554581	-0.097018	0.323237	-0.804187	0.749396
28	-0.468024	0.621288	-0.246596	-0.418987	-0.203506	-1.039601	-0.084117	0.224568	-0.106195	0.047660	-0.440081	-0.156228	0.028144	-0.140105	-0.130952	-0.296134	0.791032	-0.377422	-0.468221	-1.001112	-0.166308	-0.062962	-0.916631	0.120688	-1.327523	1.969008	-0.285732	-0.181093	0.444742	-0.236677	0.630229	1.260773	-0.081846	-0.114165	-0.858972	0.058312	0.222474	-0.185195	0.119360	-1.112345	-0.420340	-0.304459	-0.349468	0.295387	0.052197	-0.181750	0.209312	-0.065843	0.250415	-1.333774	0.738218	-0.319562	-1.477894	-0.248575	-0.814537	0.749396
29	-0.297528	-1.040557	-0.246596	-0.457084	-0.203506	-1.009796	-0.084117	1.244135	-0.038115	-0.111388	-0.338813	-0.466551	-1.008579	-0.219330	-0.130952	-0.211448	0.224856	-0.299824	-0.465751	-0.189245	0.036542	0.241414	-0.165180	0.099178	0.949661	-0.906780	-0.793500	-0.181093	-0.041642	-0.124682	-0.716828	-0.907493	-0.212870	-0.114165	-0.823174	-0.698026	3.014960	3.923277	-0.194669	0.375159	-0.368468	-0.088468	-0.253070	0.157965	-0.201126	0.158721	-0.225129	-0.065843	0.215086	0.304553	-0.021573	-0.346999	0.120525	0.428563	-0.649911	0.749396
30	-0.382776	-1.594110	-0.246596	-0.465895	-0.203506	-0.208383	-0.030192	-1.739376	-0.106195	-0.078861	-0.237545	0.010329	0.811600	-0.321829	0.250764	-0.191638	0.064723	-0.378462	-0.796007	-1.001112	-0.120380	-0.775466	-0.346827	-2.094840	0.740879	-0.103373	1.863983	1.611789	-1.938539	-0.986462	-1.356818	-0.779474	0.196700	-0.114165	-0.702449	-0.698026	-0.531923	-0.162419	1.501091	-1.540588	-0.082937	0.372409	-0.440592	0.612325	-0.253483	-0.379742	0.012412	-0.065843	-0.160290	-0.232940	-0.396663	-0.344805	-0.859163	-0.992084	-0.811783	0.749396
31	-0.672620	1.230345	-0.246596	-0.511766	-0.203506	-0.314355	0.021281	0.244117	-0.066905	-0.617603	0.268795	-0.061223	-1.053700	-0.220508	-0.130952	-0.347497	-0.305379	-0.512996	-0.559615	-0.609176	-0.166308	0.883779	-0.071939	0.077668	-1.327523	-1.012221	-0.656775	-0.181093	-0.430749	0.304238	0.881214	-0.221741	-0.217917	-0.114165	0.271248	-0.028022	-0.686047	-0.791418	-0.427051	1.102205	-0.420340	-0.304459	-0.462868	-0.379963	11.251696	-0.191002	-0.480085	-0.041656	-0.239782	0.406168	0.566064	-0.514352	0.681517	0.316103	1.301265	-1.334408
32	-0.127032	0.154516	-0.246596	-0.236367	-0.203506	-0.556104	-0.084117	-0.237094	0.531013	-0.346859	0.876402	-0.464698	-0.680984	-0.038133	0.255883	-0.365143	-1.940026	-0.675773	-0.017051	-0.357217	-0.166308	-1.334988	-0.297789	-0.632161	-0.244464	-0.789806	-0.806933	-0.181093	-1.208963	-0.672756	0.309160	-0.792007	0.074411	-0.114165	-0.870822	-0.462126	-1.156531	-0.397564	0.182166	-0.548926	0.113472	-0.099701	-0.325368	-0.148450	-0.394438	-0.777576	-0.151984	20.688705	-0.239782	-0.453998	-0.396663	-0.764028	-0.547376	-0.775766	-0.823176	0.749396
33	-0.485074	-0.331026	0.166369	-0.376572	-0.203506	-0.599155	-0.084117	-0.990491	-0.106195	-0.332065	0.775134	-0.445083	-0.201041	-0.052154	-0.130952	-0.217155	-0.572970	-0.539902	-0.255542	-0.805144	-0.166308	-0.025378	0.666849	0.002383	0.898807	-0.153187	-0.785843	-0.181093	-0.333472	-0.345623	0.584628	-0.484045	-0.217723	-0.114165	-0.317069	-0.329585	0.660512	-0.090704	-0.182108	1.102205	0.032507	-0.304459	-0.193536	-0.203543	-0.285965	-0.292774	-0.458414	-0.065843	-0.116128	-0.442410	0.551622	-0.644814	0.479992	-0.449716	0.320871	0.749396
34	0.051989	-1.176496	-0.246596	-0.393849	0.319982	-1.456866	-0.084117	-1.739376	-0.106195	0.371299	-0.946420	0.145557	1.435385	-0.101287	-0.130952	-0.085370	1.598988	0.002690	-0.075717	0.230686	-0.024696	-0.528260	-1.506004	3.648325	-1.327523	-1.405008	-0.974447	0.271786	-0.479387	0.032107	-0.514594	2.646601	0.007444	-0.114165	-1.158685	-0.698026	0.297509	-0.292637	-0.106741	-1.537362	-0.420340	-0.294616	-0.150365	-0.065501	-0.332972	0.362264	0.254426	-0.056372	-0.151458	0.289400	-0.061456	-0.691318	-0.006127	1.524139	-0.826098	0.749396
35	-0.689669	0.028367	-0.246596	-0.371734	-0.203506	-0.741555	-0.048576	-0.772442	-0.073151	-0.295360	1.787814	-0.680258	-0.443547	-0.321829	-0.130952	-0.187702	-0.487761	0.182562	0.301966	-0.861135	-0.166308	0.278203	-0.322193	0.341165	-1.327523	-0.753270	-0.273709	-0.181093	0.189391	-0.148784	1.037739	-0.263369	-0.149785	-0.114165	-0.825149	0.216389	0.419186	-0.468473	0.270095	1.102205	0.310034	0.121502	-0.403235	0.439000	-0.379385	-0.191002	-0.023066	-0.065843	-0.239782	-0.634053	0.734616	-0.618465	-1.268732	0.482485	-0.787047	0.749396
36	-0.357202	0.195736	-0.246596	-0.356444	-0.203506	-0.102411	-0.037546	0.596003	-0.106195	0.345397	0.572598	2.062345	-0.426028	-0.173059	-0.130952	-0.380755	0.291970	0.481657	-0.307168	0.118704	-0.166308	0.196948	-0.856312	-0.223472	-1.327523	-0.847663	-1.076420	-0.181093	-0.114599	-0.087449	0.044789	0.081745	-0.217917	-0.114165	-0.571356	-0.047478	-1.266040	0.061193	-0.427051	1.102205	-0.157744	1.036775	-0.089303	-0.379963	-0.242332	0.086556	-0.480085	-0.053212	-0.213284	0.595137	1.882250	-0.284766	-0.248255	1.158364	1.301265	-1.334408
37	-0.408350	-0.553438	-0.246596	-0.371734	-0.203506	-0.072606	0.009025	0.695253	-0.106195	-0.074020	-0.136277	-0.280379	-0.795111	-0.066183	-0.130952	-0.376819	0.283165	-0.621811	-0.531950	0.538635	-0.166308	1.327373	0.074023	1.239207	-1.327523	-0.049102	-0.620585	0.172625	-0.114599	-0.986462	-0.684184	1.260773	-0.217917	-0.114165	-0.430881	-0.446318	-0.621152	0.017250	-0.427051	-1.004938	-0.063696	-0.274773	1.025082	-0.379963	-0.320342	0.493642	-0.480085	-0.047395	-0.239782	0.683382	-0.396663	0.849511	0.082157	-0.623232	1.301265	-1.334408
38	-0.425400	-0.678887	0.147087	-0.180605	-0.203506	0.205571	-0.049801	2.799049	-0.015483	0.017960	-0.541349	0.099709	-0.820066	-0.192479	-0.130952	2.015256	-0.286243	1.091292	0.154993	-0.217240	0.055679	0.577815	0.329801	0.142198	0.628415	0.227874	-0.151358	-0.181093	-0.455068	-0.986462	-0.703871	-0.706959	-0.217917	-0.114165	0.344078	-0.396463	-0.304792	-0.367757	-0.427051	-0.625061	-0.420340	-0.304459	1.102456	-0.379963	-0.124513	0.878523	-0.480085	-0.055564	0.140011	-0.397842	-0.396663	1.129521	-0.379004	0.747270	1.301265	-1.334408
39	-0.195230	-0.284100	-0.246596	-0.402617	-0.100179	0.457254	0.081946	-0.798006	-0.018334	-0.224023	-0.338813	0.073718	1.397531	0.072930	0.272869	0.258820	0.078593	-0.230105	1.558398	-0.497194	-0.166308	-0.156392	0.259468	0.765987	1.010251	3.049004	0.427835	-0.181093	-0.552345	0.282043	0.767032	-0.323798	-0.117757	-0.114165	-0.518523	0.727707	-0.568426	-0.286369	0.094238	1.102205	1.040177	-0.042371	-0.437339	0.270007	-0.021208	0.528800	0.115675	-0.058652	-0.045469	-1.177785	-0.036015	-0.397422	0.531025	0.061110	-0.809243	0.749396

	AB	AF	AH	AM	AR	AX	AY	AZ	BC	BD	BN	BP	BQ	BR	BZ	CB	CC	CD	CF	CH	CL	CR	CS	CU	CW	DA	DE	DF	DH	DI	DL	DN	DU	DV	DY	EB	EE	EG	EH	EJ	EL	EP	EU	FC	FD	FE	FI	FL	FR	FS	GB	GE	GF	GH	GI	GL
0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	A	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	A	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	A	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	A	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
4	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	A	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

	AB	AF	AH	AM	AR	AX	AY	AZ	BC	BD	BN	BP	BQ	BR	BZ	CB	CC	CD	CF	CH	CL	CR	CS	CU	CW	DA	DE	DF	DH	DI	DL	DN	DU	DV	DY	EB	EE	EG	EH	EJ	EL	EP	EU	FC	FD	FE	FI	FL	FR	FS	GB	GE	GF	GH	GI	GL
0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
4	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

	AB	AF	AH	AM	AR	AX	AY	AZ	BC	BD	BN	BP	BQ	BR	BZ	CB	CC	CD	CF	CH	CL	CR	CS	CU	CW	DA	DE	DF	DH	DI	DL	DN	DU	DV	DY	EB	EE	EG	EH	EJ	EL	EP	EU	FC	FD	FE	FI	FL	FR	FS	GB	GE	GF	GH	GI	GL
0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	A	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	A	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	A	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	A	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
4	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	A	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

	AB	AF	AH	AM	AR	AX	AY	AZ	BC	BD	BN	BP	BQ	BR	BZ	CB	CC	CD	CF	CH	CL	CR	CS	CU	CW	DA	DE	DF	DH	DI	DL	DN	DU	DV	DY	EB	EE	EG	EH	EJ	EL	EP	EU	FC	FD	FE	FI	FL	FR	FS	GB	GE	GF	GH	GI	GL
0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
4	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0